Jobs Data & Analytics

Job Description

Expert knowledge as an Oracle DBA, qualification according to Oracle Database Administration 2019 Certified Professional or comparable, current project experience > 5 years
Very good knowledge of setting up Oracle WebLogic Server as well as managed servers hosting OAS and ORDS, experience > 5 years
Good knowledge of Oracle Dataguard, experience > 2 years
Advanced Linux knowledge (shell, file management, processes and devices, resource monitoring, networks)

 

Job Responsibilities

This profile focuses on the middleware and application tier. They manage the software environments that connect users to the database.
Core Responsibilities:
Managing the Oracle WebLogic Server domains, administration consoles, and managed servers hosting OAS and ORDS (Oracle REST Data Services for APEX).
Configuring data sources (JDBC connections) that bridge WebLogic to the Exadata database.
Handling application-level security, SSL/TLS certificates, deployments, patches, and monitoring server memory/JVM logs.
Scope in your stack: They ensure that the OAS reporting portal is accessible and running smoothly, and that the ORDS layer is routing traffic correctly to your APEX apps.

Department/Project Description

Client is company based in US, provides solutions and common infrastructure to enable telecommunications partners to deploy and expand next-generation technologies

Skill Category

Data Engineering

Keyskills - Must Have

• Oracle RAC
• Agile
• Linux
• Git
• Oracle-DBA
• Weblogic
• TLS/SSL

Automat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We’re looking for an AI Engineer (Senior level or strong Middle) to join our team and work directly with startup's Data Science and R&D teams. This is a hands-on, delivery-focused role where you will own projects end-to-end, from early design to production deployment.

The role is focused on building production-grade Generative AI systems on AWS, especially RAG pipelines, agent-based workflows, and LLM-powered backend services. This is not a pure research or model training role, it’s about designing and shipping reliable systems under real constraints.

📍 Work location: remote from Ukraine.

If you are interested in this opportunity, please submit your CV in English.

Key Responsibilities

• Build and deliver production-ready GenAI systems on AWS, including RAG pipelines, retrieval logic, and LLM-based services
• Design and implement agent-based or multi-step workflows for real use cases (not just prototypes)
• Work closely with Solution Architects, DevOps, and client teams to turn ideas and POCs into scalable systems
• Deploy, monitor, and improve ML/LLM systems in production, focusing on performance, cost, and reliability
• Work with AWS services such as Bedrock, OpenSearch, Lambda, S3, DynamoDB, SageMaker, and CloudWatch
• Adapt existing ML or GenAI code into production environments when needed
• Continuously improve system quality, including retrieval performance, output consistency, and evaluation approaches

• Operate in a fast-paced, project-based environment where you may own a project as the main engineer

   

Requirements

• Strong hands-on experience building and deploying AI / GenAI systems in production
• Solid experience with AWS in real projects (not just Console usage), including working with services like Bedrock, OpenSearch, Lambda, S3, or similar
• Experience building RAG systems in practice, including retrieval logic, vector databases, and output quality improvements
• Experience working with LLM frameworks or tools such as Amazon Bedrock, OpenAI, LangChain, or LlamaIndex
• Strong Python skills and a good understanding of backend system design
• Experience designing multi-agent systems or more complex orchestration workflows
• Experience with vector databases (OpenSearch, pgVector, Pinecone, etc.)
• Comfort working in fast-moving environments with short project cycles (weeks to a few months)
• Strong communication skills and ability to work directly with clients and cross-functional teams
• Ability to clearly explain technical decisions, limitations, and trade-offs in English (written and spoken)
• Experience with AWS Bedrock advanced features (e.g. Knowledge Bases, agent orchestration) - an advantage 
• Experience with Kubernetes, Docker, IaC Terraform and CI/CD pipelines  - an advantage 

• Background in Machine Learning or Data Science (including model training or fine-tuning)  - an advantage

   

What Success Looks Like in Year One

• Within 12 months, you are independently owning and delivering production GenAI systems on AWS, from initial design through deployment and iteration
• Working directly with customers and making clear architectural decisions around performance, cost, and reliability

• You are comfortable taking an ambiguous problem, turning it into a working system end-to-end, and explaining the trade-offs behind your approach

   

Why Join Automat-it?

Be at the Forefront of Cloud Innovation

Join a global leader and AWS Premier Tier Services Partner. We don't just use the cloud; we master it. As an All-In AWS partner, you’ll work with the most sophisticated tech stacks and help maintain our status as a top-tier collaborator in the global ecosystem.

Work on Meaningful, Large-Scale Impact

We specialize in helping startups and scaleups scale at speed. By joining us, you’ll be part of a team that has successfully completed hundreds of complex projects, directly influencing the growth of the next generation of unicorns through our unique "Product-based Professional Services" approach.

Accelerate Your Career Trajectory

In our fast-paced, dynamic environment, "growth" isn't just a buzzword. You will have:

• Clear Progression: Defined career paths from engineering to leadership.
• Continuous Learning: Sponsored AWS certifications and access to internal "Expert-led" knowledge sharing.
• Cutting-Edge Exposure: Early access to beta AWS features and high-level architectural challenges.

Thrive in a Culture of Excellence

We pride ourselves on a collaborative, supportive, and transparent culture. You’ll work alongside and learn from some of the industry’s brightest architects and DevOps engineers who are committed to mentorship and collective success, including AWS Gold Jacket recipients.

Global Reach, Boutique Feel

With over 250 employees across EMEA and the USA, enjoy the stability of a mature, profitable company with the agility and spirit of a startup. Whether you are working on GenAI & Agentic use cases, DevOps automation, FinOps optimization, or Kubernetes migrations, your voice is heard, and your contributions are visible.

Benefits

• Professional training and certifications covered by the company (AWS, FinOps, Kubernetes, etc.)
• International work environment
• Referral program – enjoy cooperation with your colleagues and get a bonus 
• Company events and social gatherings (happy hours, team events, knowledge sharing, etc.)
• English classes
• Soft skills training

Country-specific benefits will be discussed during the hiring process.

Automat-it is committed to fostering a workplace that promotes equal opportunities for all. We firmly believe that cultivating a diverse workforce is crucial to our success. Our recruitment decisions are grounded in your experience and skills, recognizing the value you bring to our team.

We are looking for a Prompt Spec Engineer who will define how our AI systems communicate, reason, and deliver high-quality outputs across products powered by LLMs.
 

You will be responsible for designing prompt/spec architectures, building evaluation systems, and ensuring that AI responses are consistent, reliable, and production-ready across RAG systems, AI agents, and automation workflows.

What you will do:
 

• Design and evolve prompt/spec architecture for AI products, LLM services, RAG systems, and AI agents, defining prompt structures, model behavior rules, response formats, and quality criteria.
• Develop, optimize, and adapt prompts for different LLMs and use cases, considering output quality, context limitations, cost, and system stability.
• Build and maintain AI evaluation processes including test scenarios, golden datasets, regression testing, and A/B experiments for LLM performance measurement.
• Analyze AI outputs, identify root causes of issues (hallucinations, missing context, retrieval problems, incorrect or unsafe responses), and define improvements.
• Collaborate with AI Engineers and Product Managers to implement prompt solutions into production systems, RAG pipelines, AI agents, and automation workflows, translating business needs into technical specifications.
• Work with LLM evaluation and monitoring tools (such as PromptFoo, Langfuse, and similar solutions) to track metrics, prompt versions, response quality, and system performance.
• Create and maintain structured documentation: prompt specs, evaluation frameworks, usage guidelines, limitations, and best practices for safe and reliable AI behavior.

What we expect from you:
 

• 2+ years of experience in LLM-related roles such as Prompt Engineering, AI Product Operations, AI Evaluation, or similar.
• Hands-on experience designing and optimizing prompts for different LLMs with a strong understanding of model behavior, strengths, and limitations.
• Experience building prompt/spec systems including prompt structure design, behavior rules, response formatting, testing, and versioning.
• Strong experience in AI evaluation: test case design, output analysis, issue detection, and iterative improvement of model performance.
• Understanding of RAG systems, AI agents, structured outputs, tool calling, and principles of reliable LLM system design.
• Ability to collaborate with engineering and product teams, translate business requirements into AI specifications, and document solutions clearly and consistently.
• Upper-Intermediate (B2+) English 
   

What we offer

• Competitive compensation depending on experience
• Work on production-level AI systems (LLMs, RAG, AI agents, automation workflows)
• Office-based work in Lviv with the possibility of a hybrid schedule.
• Direct impact on AI product quality and system behavior
• Access to modern LLM tools and evaluation frameworks
• Professional growth in a fast-moving AI-focused environment
• Opportunity to shape prompt engineering standards inside the company
• Relocation support for candidates from other cities, including assistance with moving and adaptation in Lviv.

The Mission
Are you a cloud architect who finds beauty in a perfectly orchestrated pipeline? Do you believe that “manual” is a four-letter word? We are looking for a Senior SRE specialized in BI Ecosystems to join our mission-critical Infrastructure team.
In this role, you won’t just “maintain” systems; you will be the master weaver connecting our advanced Data Analytics tools with a rock-solid, automated GCP/AWS backbone. You will bridge the gap between traditional Business Intelligence and modern Cloud Engineering, ensuring our data flows are fast, our visualizations are seamless, and our infrastructure is invisible.

What You Will Do
1. Master the Orchestration (GCP & GKE)
You will take full ownership of our Google Kubernetes Engine (GKE) clusters. This isn’t just
about pods and services; you’ll manage advanced networking (VPC Service Controls, Shared VPC) and high-availability node configurations to ensure our data platforms never skip a beat.
2. Advance Data Architectures (Lakehouse)
You will design and support a unified Data Lakehouse architecture by integrating BigQuery and Cloud Storage. Your mission is to break down silos, ensuring that our raw data and analytical models coexist in a single, high-performance ecosystem. You will implement robust governance and security policies that ensure data quality and accessibility, providing a “single source of truth” that is both scalable and cost-effective
3. Evangelize Infrastructure as Code (IaC)
Our goal is 100% immutability. You will use Terraform to automate the entire lifecycle of our
GCP resources—from Compute Engine and Cloud Run to Cloud SQL—ensuring consistency
across Dev, Staging, and Production.
4. Empower DataViz Excellence
You will be the enabler for our Data Analysts. You’ll deploy, optimize, and scale Qlik Sense,
Tableau, or Similars environments on GCP, handling everything from SSO/IAM authentication to high-performance connectivity with our backend data sources.
5. Hybrid Cloud & Legacy Modernization
Complexity is our playground. You will manage critical AWS resources (EC2, S3) and ensure
seamless interoperability with GCP. Simultaneously, you will lead the charge in migrating legacy monolithic systems into modern, microservices-based architectures.
6. Data Engineering Collaboration
Working side-by-side with Data Engineers, you’ll help build and maintain robust pipelines using Cloud Dataflow, Cloud Composer (Airflow), and BigQuery. You ensure that the “plumbing” of our data world is leak-proof and high-performing.
7. Reliability, Security & FinOps
● CI/CD: Design sophisticated pipelines (GitLab CI/GitHub Actions) integrating DevSecOps best practices.
● Observability: Implement deep monitoring using Google Cloud Operations Suite and Grafana.
● FinOps: Be the guardian of our budget, identifying and eliminating cloud waste through
smart optimization.

Who You Are (Requirements)
● Cloud Expert: Proven experience managing complex environments in Google Cloud Platform (GCP) and AWS.
● Data Architect: Deep understanding of Data Lakehouse models and governance.
● Kubernetes Pro: Deep knowledge of GKE, including advanced networking and security policies.
● Terraform Ninja: Ability to write modular, scalable, and secure IaC.
● BI Infrastructure Savvy: Experience deploying and tuning enterprise visualization tools (Tableau, Qlik, or Power BI).
● Automation Mindset: Strong scripting skills (Python, Bash, or Go) and mastery of CI/CD engines.
● Data-Driven: Familiarity with Big Data tools like Airflow, BigQuery, and Dataflow.
● Security-First: Solid understanding of encryption, IAM roles, and network security standards.

Why Join Us?
● Impact: You are not a cog in the machine; you are building the machine.
● Tech Stack: Work with the latest tools in the Cloud and Data world (Lakehouse, GKE).
● Culture: We value technical excellence, proactive problem-solving, and a “fail fast, learn
faster” mentality.
● Growth: We invest in your certifications and continuous learning. Ready to redefine how BI meets SRE? If you are a builder who loves solving the puzzle of high-scale data infrastructure, we want to meet you.

Apply now and let’s build the future of data infrastructure together!

About Our Client
 

Our client builds and operates GPU cloud infrastructure at the scale modern AI demands. They run dense accelerated-compute clusters on bare metal and Kubernetes, stitched together with high-performance Ethernet and InfiniBand fabrics, and deliver them to customers as reliable, secure, high-throughput platforms. They are building Cloud v2, their next-generation, clean-slate, Kubernetes-native GPU cloud, and scaling it across new data center sites. The infrastructure team sits at the center of that effort, building the systems that turn racks of hardware into a product customers depend on.
 

The role

We are hiring a Senior Observability Engineer to build the observability layer for Cloud v2. This role exists to close the single most damaging gap in our client’s current product: today their observability is reactive, so customers are their failure detector. Some of their worst incidents went undetected internally because they occurred below the layer they instrument (a corrupted forwarding entry, a flapping firewall sync process), and the customer reported the outage before their dashboards did. Cloud v2 is architected from day one against the industry’s Gold-tier bar for neocloud platforms (SemiAnalysis ClusterMAX), and proactive detection, where they see problems before the customer does, is the trust gap they are building this platform to fix.
 

You own the metrics, logs, alerting, dashboards, and detection stack that make that true. That means operator-facing observability that catches failures first and drives automated remediation, and tenant-facing observability that gives customers the usage, health, and status visibility a hyperscaler-grade cloud implies. This is the observability workstream the platform validates earliest, because detection is where the current product loses trust, and it maps directly to the ClusterMAX Monitoring and Reliability criteria.
 

This is a build-focused, hands-on senior IC role. You will spend your time engineering the telemetry pipelines, detection logic, dashboards, and alerting that turn raw signals from multi-vendor GPUs (AMD/NVIDIA), hosts, fabrics, and switches into problems we catch before customers feel them, and you will set the technical direction and standards for how we instrument the platform. You will partner closely with our client’s SRE, platform, network, and security engineers, who own the control-plane architecture, the fabric, and day-to-day operations; you own the observability that makes the whole platform legible and its failures visible.
 

What you’ll own

The OpenTelemetry foundation: own OpenTelemetry as the instrumentation standard the whole platform plugs into: the collector topology and pipelines, semantic conventions for metrics, logs, and traces, and consistent resource and tenant attribution across GPU, host, fabric, switch, and control-plane signals. Everything we instrument, internal and tenant-facing, lands on this common backbone so signals are portable, correlated, and not locked to any one backend.

Proactive detection: build the detection that fires before a customer reports, GPU failure-rate tracking, Xid detection, and the anomaly and threshold signals that feed the automated detect-drain-remediate loop, so mean time to detect stops being inverted and our client sees the problem first.

The metrics pipeline: own the metrics stack across GPU, CPU, network planes, thermal, and power (Prometheus and OpenTelemetry-based), including the DCGM profiling-metric suite (DCGM_FI_PROF_* for SM activity and tensor TFLOPs, plus PCIe AER, ECC, power and thermal, and NVLink and fabric throughput), with custom exporters and OpenTelemetry receivers where DCGM falls short.

Instrumenting below the layer we watch today: push instrumentation down into the sub-layers where our most damaging silent failures live (forwarding state, firewall and HA sync, fabric health), so the failure classes that used to reach us through the customer surface on our own dashboards instead.

Host and switch debug data: build reliable extraction of host and switch debug and telemetry data so responders have the signal they need without hand-collecting it during an incident.

Operator and tenant dashboards and alerting: build the operator dashboards and low-noise, actionable alerting the on-call team runs on, and a tenant-facing status page so customers are not our failure detector.

Tenant-scoped observability: deliver per-tenant usage and utilization visibility and per-tenant access to logs and metrics, cleanly isolated from internal ops telemetry, on a multi-tenant model where no tenant can see another’s data.

Proactive customer notifications: build automated, proactive maintenance and change notifications to affected tenants, replacing today’s manual and ad hoc comms.

Reliability and SLO instrumentation: build the telemetry behind the 99.9%+ node SLA measurement, the passive and active health-check battery, and MTTA and MTTR tracking, so reliability is measured, not asserted, and so the health automation has trustworthy signal to act on.

AI-leveraged observability: push our observability toward an AI-augmented model, agentic triage, anomaly detection, alert-noise reduction, and MCP-driven access to our operational telemetry, so a small team catches more, faster, than headcount alone would allow.

Technical direction and standards: set the instrumentation patterns, metric and label conventions, alerting standards, and SLO practice the rest of the platform adopts, and raise the bar through design review and mentorship.
 

What we’re looking for

Required

8+ years building production observability or monitoring systems at scale, ideally for cloud, hosting, or high-performance compute environments, with real platform-build experience (not solely operating dashboards someone else built).

Deep expertise with Prometheus and the metrics ecosystem: PromQL, exporters, recording and alerting rules, Alertmanager, and Grafana, used to build durable, reusable observability rather than one-off dashboards.

Hands-on experience designing and running OpenTelemetry as a foundational instrumentation layer: the Collector, receivers and exporters, semantic conventions, and vendor-neutral pipelines that multiple backends and teams plug into.

Experience running time-series at scale, including long-term and high-cardinality storage (Thanos, Mimir, Cortex, or VictoriaMetrics), and a real understanding of cardinality, retention, and query-performance tradeoffs.

A track record of building alerting that is actionable and low-noise, and of practicing SLO and error-budget-based reliability rather than alert on everything.

Strong programming skills in a real language (Python, Go, or similar) used to build maintainable exporters, collectors, and observability tooling, not just glue scripts.

Strong Linux systems fundamentals: you can reason about the OS, networking, storage, and the boot and provisioning path, and instrument at that level, not just at the application surface.

A track record of owning significant observability systems end to end and setting technical direction that other engineers adopt: defining standards and influencing architecture across a team.

A demonstrated bias toward building things right the first time: reproducible, code-defined observability and sound instrumentation design over short-term shortcuts.

Strong written communication: you document designs and decisions clearly, and you raise the team’s bar through design review and mentorship.
 

Strongly preferred

Multi-vendor GPU/HPC observability: DCGM & dcgm-exporter (NVIDIA) and RDC & AMD Device Metrics Exporter (AMD/ROCm); NVML and AMD SMI; Xid vs. amdgpu/RAS error reporting; DCGM_FI_PROF_* and ROCProfiler profiling metrics; GPU node health checks (DCGM diagnostics, AGFHC, ROCm Validation Suite).

Experience building multi-tenant, tenant-facing observability with per-tenant metric and log isolation.

Checkmk (our client runs parent and child sites today), and experience migrating or bridging a Checkmk estate into a modern metrics stack.

Network and fabric observability: switch telemetry, streaming telemetry (gNMI) and SNMP, BGP and fabric health, and RoCE Ethernet or InfiniBand/UFM fabric monitoring.

Log and trace pipelines and distributed tracing: Loki, Elastic, or Vector, and instrumenting traces across a distributed control plane.

Kubernetes observability: kube-state-metrics, cAdvisor, and GPU Operator metrics, in a multi-cluster environment.

Status-page and incident tooling, MTTA and MTTR instrumentation, and integration with an incident-response and paging workflow.

Building observability within a SOC 2, ISO 27001, or HIPAA compliance posture, including audit-log export.
 

How we work

Small, senior, high-trust teams. We hire people who can own and solve problems end to end and we give them the room to do it.

Code over clicks. The platform is defined, reviewed, and versioned. Manual changes are the exception and they get automated away.

Build it right. We invest in sound architecture and durable design so the platform scales cleanly instead of accumulating debt.

Customers are real. The platform exists to serve customer workloads, and we build to the reliability and self-service standard a hyperscaler-grade cloud implies.

We document. Designs, decisions, and architecture are written down so the team compounds knowledge instead of re-learning it.
 

AI as a force multiplier

We expect our engineers to use AI to move faster and operate at higher leverage, and we build our environment to make that the default rather than an afterthought. We maintain CLAUDE.md and AGENTS.md context files across our repositories, run MCP servers against our operational systems (Jira, NetBox, Checkmk), and use agentic coding tools such as Claude Code directly in our infrastructure and platform workflows.
 

For this role in particular, observability is one of the highest-leverage places to apply AI: anomaly detection, automated incident triage, alert-noise reduction, and AI-assisted root-cause analysis over the telemetry you build. You don’t need to be an AI expert, but you should be eager to fold these tools into how you design and operate observability, and to help us define what an AI-leveraged infrastructure team can do.

What success looks like
 

First 30 days

Ramped on the Cloud v2 architecture, the current observability stack (Prometheus, Checkmk parent and child sites, and the GPU and fabric telemetry), and the lab, and able to navigate the repos and design docs independently.

Shipped your first meaningful contribution to the metrics, alerting, or detection stack with the team’s review.

Aligned with SRE, platform, network, and security on where your work meets theirs and on the highest-value detection gaps to close first.
 

First 90 days

Owning a defined area of the v2 observability layer end to end, from design through delivery (for example the OpenTelemetry instrumentation foundation, the GPU and DCGM metrics pipeline, the alerting and SLO framework, or the tenant-facing dashboards and status page).

Delivered proactive detection for at least one failure class that the platform previously learned about from customers, so our client now detects it first.

Measurably reduced alert noise or improved mean time to detect for a meaningful part of the fleet.
 

First 12 months

The go-to engineer for the hardest observability and detection problems in Cloud v2, trusted across SRE, platform, network, and security, and setting the standards the team instruments by.

Designed and delivered a significant piece of the platform’s observability (proactive detection, tenant-facing observability, or reliability and SLO instrumentation) that raised the whole team’s leverage and moved the platform toward its Gold-grade Monitoring and Reliability bar.

We’re looking for an AI Automation Engineer to build and launch an automated video production pipeline for Counter-Strike 2 content.

The goal is to automate the full workflow for YouTube Shorts, Instagram Reels, and TikTok — from content sourcing and AI processing to editing, voice-over, subtitles, publishing, and performance tracking.

Responsibilities

• Design and build an end-to-end video automation pipeline from scratch;
• Build complex n8n workflows with schedules, queues, conditions, parallel branches, retries, and error handling;
• Integrate LLMs and AI APIs such as OpenAI, Anthropic, and others;
• Integrate video generation, processing, and editing services via API;
• Automate AI avatars, lipsync, voice-over, dubbing, and character modifications;
• Automate video editing: different sources, scenes, hooks, subtitles, and voice-over;
• Set up scheduled publishing via YouTube Data API, Instagram Graph API, and other platform APIs;
• Work with OAuth, multiple accounts, API limits, and logging;
• Implement automated pre-publish checks: descriptions, hashtags, stop-lists, and AI-content labels;
• Build an operator dashboard for content calendar, queues, errors, providers, and content types;
• Collect performance metrics and optimize content distribution based on results;
• Monitor, troubleshoot, and scale the pipeline to new platforms and channels;
• Maintain technical documentation: README, ARCHITECTURE, RUNBOOK.

Requirements

• Hands-on experience building automation workflows / pipelines from scratch;
• Strong n8n experience with complex production workflows;
• Knowledge of Python or JavaScript for custom scripts;
• Experience with REST APIs, Webhooks, and OAuth;
• Experience integrating LLMs / AI APIs such as OpenAI or Anthropic;
• Experience with video generation, processing, or editing APIs;
• Experience with social media publishing APIs such as YouTube API / Instagram Graph API;
• Ability to independently break down a task, choose the right tools, build the solution, and bring it to stable production;
• English sufficient for reading technical documentation.

Nice to have

• Experience with AI content generation pipelines;
• Experience with FFmpeg or similar video-processing tools;
• Experience with AI avatars, lipsync, TTS/STT, voice cloning, or dubbing;
• Experience automating TikTok, Reels, or YouTube Shorts;
• Experience with Zapier, Make (Integromat), or similar automation tools;
• Understanding of monitoring, logging, queues, and fault-tolerant workflows.

What We Value

We’re looking for someone who can own the entire automation flow — from architecture and tool selection to MVP, production launch, optimization, and scaling.

We value autonomy, ownership, and a result-oriented mindset.

And if you play CS2 / CS, that’s an extra bonus — we also run internal CS2 tournaments 🎮

Responsibilities

• Create and maintain reporting and analytical dashboards that provide key business insights
• Search for insights and patterns through data analysis
• Based on the analysis results, make conclusions for the business
• Build interactive dashboards

Requirements

• 4+ years of experience as a Data Analyst
• 4+ years of hands-on experience with SQL
• Experience with data visualization tools (Tableau, Power BI, Looker or similar)
• Strong logical and critical thinking skills
• Attention to detail and structured approach to work
• English Intermediate and higher
• Proactive mindset and genuine interest in your work

Will be a plus

• Experience with Python for data analysis and scripting
• Experience with R for statistical analysis
• Familiarity with n8n or similar workflow automation tools

We offer

• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays according to current legislation
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

AI courses for business people — from beginner to advanced — in English.

Duties

• Write lesson scripts that are clear, practical and enjoyable to read — no theory for theory's sake.
• Create the visuals: diagrams, AI-generated illustrations, annotated screenshots.
• Script and produce videos: click-by-click screen tutorials and narrated lesson videos (HeyGen, Synthesia or similar).
• Design practice assignments, self-check questions and a capstone project for each course.
• Test every prompt and tool step before it ships; keep content 100% original and free of unsourced statistics.
• Revise quickly on founder feedback.

Requirements: daily user of ChatGPT, Claude, Gemini and image tools; native-level English; portfolio with at least one course or five lessons and one video you produced yourself.

Terms: paid per module, first module is a paid pilot, start immediately.

About the work

We build and maintain industry intelligence databases - organizations, people, products and their relationships across sectors such as HealthTech, QuantumTech, LegalTech and wellness, covering multiple regions. Every one of them starts as messy public data and has to end up as a clean, deduplicated, verifiable dataset that analysts and downstream products can rely on.

That means the hard problems here are not "write a scraper". They are: how do you know two records are the same organization? How do you prove where a field came from six months later? How do you re-run a collection over a source that changed its markup, without corrupting what you already have?

You will work directly with the Head of Data Science and a team of AI and data engineers.

What you will do
 

• Build and own collection pipelines over public web sources - structured, semi-structured and awkward
• Design the enrichment layer: normalization, deduplication, entity resolution, confidence scoring
• Keep provenance on every field, so any value can be traced back to its source and date
• Use LLMs where they genuinely help - extraction from unstructured text, classification, taxonomy assignment - and know where they don't
• Take datasets from one-off collection to scheduled, monitored, re-runnable pipelines
• Define and enforce quality checks: coverage, freshness, duplicate rate, field completeness

What we're looking for

Both levels
 

• Strong Python - you write services and pipelines, not notebooks
• Real production scraping experience: sessions, rate limits, retries, blocks, anti-bot, JS-rendered pages, APIs that are not documented
• SQL and PostgreSQL beyond CRUD - indexing, query plans, schema design
• Practical experience with LLM APIs for extraction and classification, including how you validate their output
• Data quality instinct: you check what you collected before you hand it over

Additionally for the Senior opening
 

• You have owned a data platform, not just tasks on one - orchestration, scheduling, monitoring, backfills
• Experience with entity resolution or record linkage across sources that disagree with each other
• Airflow, Dagster or Prefect in production
• You have set the standards other people then worked to: schemas, conventions, review

Nice to have
 

• Docker and CI/CD
• Knowledge graphs, ontologies, or taxonomy design
• Vector search and RAG pipelines
• OSINT methodology and source verification
• Experience with data licensing or compliance constraints on public-web collection

How we hire
 

1. Intro call - 30 minutes, mutual fit and what you've actually built
2. Paid proof task - a real, bounded piece of our work. Paid at market rate, roughly 4 hours. We are not asking anyone to work for free
3. Technical review - we go through your solution together: your trade-offs, what you'd do with more time, what you'd do differently at ten times the volume
4. Offer - band determined by the level you land at, not by the title you applied under

We evaluate the proof task on judgment, not polish. Reproducibility, honest handling of edge cases, and clear reasoning about what you chose not to do count for more than a clever one-liner.
What we care about

Two things, mostly.

You question your own data. The engineers who work out well here are the ones who notice that a source silently changed its schema, that a "97% match rate" is hiding a systematic bias, that a field is populated but wrong. Volume without verification is worth nothing to us.

You explain your reasoning. Much of this work involves judgment calls that nobody can check quickly - which record wins a merge, what confidence threshold to use, when to stop enriching. We need those decisions written down and defensible, not buried in a script.

To apply: send your CV plus one short paragraph on the hardest data collection or deduplication problem you have solved, and what you got wrong on the way.

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

PwC SDC Lviv, opened in 2018, is part of this global space. It is a place where technology is combined with team spirit, and ambitious ideas find their embodiment in real projects for Central and Eastern Europe.

What do we guarantee?

• Work format: Remote or in a comfortable office in Lviv — you choose.
• Development: Personal development plan, mentoring, English and Polish language courses.
• Stability: Official employment from day one, annual review of salary and career prospects.
• Corporate culture: Events that unite the team and a space where everyone can be themselves.

Turn your curiosity for automation and AI into a technology career. 

We are looking for self-driven professionals from Assurance who have demonstrated a passion for improving the way work gets done through Python, automation, data analytics, Copilot, or Generative AI. Whether you have built scripts, automated repetitive tasks, experimented with AI tools, or simply enjoy exploring new technologies, this role offers an opportunity to transform that passion into a full-time technology career. 

As a Junior AI Engineer, you will learn how to design and develop scalable AI-powered solutions while combining your audit and business knowledge with modern engineering practices. Working alongside experienced AI engineers, you will gain hands-on experience with Python, Generative AI, cloud platforms, intelligent automation, and enterprise AI solutions. 

This is a career opportunity for professionals who want to evolve from being consumers of technology into creators of technology, helping shape how AI is used across PwC and for our clients. While previous AI engineering experience is not required, a genuine interest in technology, continuous learning, and hands-on experimentation is essential. 

Key Responsibilities 

AI Solution Development:

• Develop AI and automation solutions using Python. 
• Build and maintain integrations with LLM APIs and AI services.
• Support implementation of RAG-based applications.
• Assist in developing prompt engineering strategies and reusable AI components.
• Participate in testing, debugging, and improving AI solutions. 

Engineering Excellence:

• Write clean, maintainable, and well-documented code.
• Participate in code reviews and technical discussions.
• Create and maintain REST APIs and backend services.
• Support CI/CD pipelines and deployment activities.
• Follow secure coding and software engineering best practices. 

Data & AI Operations:

• Prepare and process structured and unstructured data.
• Assist with document processing, OCR, chunking, and embedding pipelines.
• Work with SQL and vector databases.
• Monitor AI solution performance and troubleshoot production issues. 

Learning & Innovation: 

• Continuously learn emerging AI technologies and frameworks.
• Contribute to internal proof-of-concepts and AI adoption initiatives.
• Share knowledge with the broader AI engineering community. 

Required Qualifications 

Technical Skills: 

• 1–3 years of experience in software development or automation.
• Good Python programming skills.
• Understanding of object-oriented programming and software engineering principles.
• Experience working with APIs and JSON data structures.
• Basic knowledge of Git and version control workflows.
• Understanding of relational databases and SQL.
• Familiariaty with cloud concepts (Azure preferred).
• Interest in Artificial Intelligence and Generative AI technologies. 

AI Knowledge:

• Experience using AI IDEs (Cursor/Copilot/Claude/and so on) during daily work.
• Basic understanding of:  Large Language Models (LLMs), Prompt Engineering, Embeddings, RAG concepts, Vector search fundamentals.

Nice-to-Have Skills

• LangChain, LangGraph, CrewAI, Semantic Kernel 
• Azure AI services
• Azure AI Search
• Azure Document Intelligence
• Azure AI Foundry
• FastAPI or Flask
• Docker containers
• Kubernetes basics
• Vector databases (Qdrant, pg_vector, Azure AI Search)
• Async Python programming
• Event-driven architectures and messaging systems
• Pandas and data processing libraries
• GitHub Actions or Azure DevOps pipelines 

Policy statements:
www.pwc.com/ua/uk/about/privacy.html

Key Responsibilities  

• Design, build, and maintain scalable, reliable data pipelines (batch and streaming).  
• Develop and optimise data models, warehouses, and lakehouse architectures.  
• Build and automate CI/CD pipelines for data workflows and deployments.  
• Ensure data quality, integrity, lineage, and governance across environments.  
• Implement proactive monitoring and alerting for pipelines and data infrastructure (CloudWatch, Grafana).  
• Manage IaC for consistent, reliable data infrastructure.  
• Troubleshoot and optimise pipeline and query performance across environments.  
• Lead incident resolution and root cause analysis for data systems.  
• Collaborate with analysts, data scientists, and engineering teams, ensuring security, scalability, and operational excellence to understand data requirements and translate them into technical solutions  
• Document data architecture, lineage, and workflows  

Must have  

• 5+ years in data engineering, with solid DevOps/platform engineering exposure.  
• Strong SQL and data modelling skills (dimensional, normalised, lakehouse).  
• Proficient in Python; working knowledge of Scala, Java, or Go.  
• Hands-on experience with data pipeline/orchestration tools (Airflow, DBT).  
• Experience with distributed processing frameworks (Spark, Flink, or similar).  
• Advanced AWS expertise (Glue, EMR, Redshift, Athena, S3, Lambda, Kinesis).  
• CI/CD tools (GitHub Actions, GitLab CI, Jenkins, CircleCI).  
• Infrastructure as Code (CDK, CloudFormation).  
• Experience with streaming/event-driven architecture (Kafka, Kinesis, Apache Flink, or Pub/Sub).  
• Observability tools: Grafana, CloudWatch.  
• Expertise with data warehouse platforms (Redshift, Snowflake).  

Desirable  

• Experience with containerisation and orchestration (Docker, ECS, Kubernetes).  
• Understanding of data governance, cataloguing, and security/compliance (GDPR, lineage tools).  
• Experience with GitHub Enterprise, Atlassian APIs, and Slack bots.  
• Understanding of cost optimisation strategies in multi-cloud and data warehouse environments.  

We are looking for a Middle AI/ML Engineer to develop computer vision and language-model capabilities for digital accessibility solutions. You will work on production-ready AI systems that help make websites and applications more usable for people of all abilities, combining classical computer vision, YOLO-based models, LLM integrations, embedding pipelines, vector search, and RAG APIs.
 

Required for this role

• 3+ years of experience in machine learning or AI engineering, with a focus on deploying models to production
• Strong knowledge of computer vision, including classical algorithms, object detection, and classification
• Hands-on experience with YOLO models for classification and object detection
• Proficiency in Python and common ML libraries such as TensorFlow, PyTorch, Scikit-learn, or Hugging Face Transformers
• Experience working with LLMs, API integrations, embedding pipelines, vector databases, and RAG
• Strong back-end engineering skills, including API design, cloud service integration, and infrastructure supporting ML workflows
• Experience building and maintaining scalable ML infrastructure and data pipelines using tools such as AWS Glue, Athena, Spark, or similar
• Strong understanding of the ML model lifecycle, including training, evaluation, deployment, monitoring, and continuous improvement
• Familiarity with HTML, CSS, and JavaScript, together with an interest in digital accessibility
• Strong communication skills and the ability to explain complex ML concepts to technical and non-technical audiences
   

Your responsibilities

• Develop and improve computer vision solutions using classical algorithms and YOLO-based classification and object detection models
• Fine-tune LLMs and build embedding pipelines for production use cases
• Implement and validate vector search using vector databases
• Develop RAG modules and APIs, and integrate AI capabilities with existing systems
• Process and collect data for AI systems, building and maintaining pipelines for developing and deploying algorithms at scale
• Research current approaches in AI/ML and apply relevant findings to digital accessibility challenges
• Design, evaluate, and iteratively improve algorithms using rigorous statistical and domain-relevant metrics
• Contribute to technical documentation, code reviews, team planning, demos, presentations, and cross-functional collaboration

What you get
Your time off

• 18 paid vacation days and 10 paid sick days annually
• 10 Ukrainian public holidays
• Maternity and paternity leaves
• Marriage and Parenthood Package
• Additional leave for major life events
• Sabbatical leave opportunities
   

Learning & growth

• Sombra University workshops and internal learning programs
• Tech Communities and knowledge sharing sessions
• Language courses and workshops
• Mentorship opportunities
   

Health & well-being

• Sports compensation or health insurance coverage
• Participation in races and marathons
• Corporate doctor (telemedicine)
• Well-being initiatives and workshops
   

And even more

• Company-provided technical equipment
• PE administration and tax support
• Internal referral program
• IT Club loyalty program
• Company events and volunteering initiatives

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the role:
We are looking for an ML Engineer / Applied Scientist to join a multidisciplinary team working on a network intelligence and reliability project for a distributed wireless sensor network.
The role focuses on evaluating the feasibility and potential value of machine learning and advanced analytics for network-state understanding, degradation prediction, capacity forecasting, decision support and predictive maintenance.
You will work with real-world network telemetry, historical operational data, simulator-generated data and controlled field experiments.

Responsibilities:
ML & Data feasibility
- Assess the availability and quality of historical data for ML use cases
- Analyze data coverage, class/event distribution, temporal coverage and labeling quality
- Identify data gaps and determine whether additional instrumentation or data collection is required
- Evaluate whether the available data contains sufficient signal for predictive or classification tasks
- Define appropriate training, validation and test strategies for temporal and network data
- Avoid data leakage and ensure that evaluation reflects realistic deployment conditions
Network-state analytics
- Work with the Network Architect and Data Engineer to define an analysis-ready representation of network state
- Analyze network states based on variables such as: topology, node/link status, routing, RSSI/SNR, packet loss, latency, traffic/load, battery state, device availability
- Develop analytical and ML approaches for identifying healthy, degraded, constrained or unstable network states
- Investigate temporal patterns and transitions between network states
Baseline development
- Establish reproducible statistical and deterministic baselines
- Define appropriate evaluation metrics for each hypothesis
- Compare candidate ML models against simple baselines
- Quantify whether ML provides meaningful improvement over existing approaches
- Analyze trade-offs between accuracy, complexity, interpretability and implementation requirements
Simulator-based experiments
- Work with the Network/Embedded Engineer to use the existing network simulator for controlled experiments
- Analyze simulator-generated datasets
- Compare simulated and real-world data distributions
- Assess whether synthetic data can safely be used for model development and evaluation
- Support scenario generation and experiment design
- Evaluate whether simulator results generalize to observed real-world network behavior
- Help identify simulator calibration requirements

Requirements:
- 4+ years of professional experience in Machine Learning, Applied Data Science, Data Science Engineering or a related field
- Strong Python skills and hands-on experience with ML/data science workflows
- Strong understanding of: supervised and unsupervised learning; classification; regression; time-series analysis; anomaly detection; forecasting; model evaluation.
- Experience working with temporal and event-based datasets
- Strong understanding of data leakage, train/validation/test splitting and evaluation methodology
- Experience building reproducible ML experiments
- Strong statistical fundamentals
- Experience establishing and evaluating non-ML baselines
- Ability to work with imperfect, noisy and incomplete real-world data
- Ability to translate a technical/business hypothesis into a measurable ML experiment
- Ability to communicate model results, limitations and uncertainty to technical and non-technical stakeholders

Nice to have:
- Network / Telemetry / IoT
- Experience working with data generated by: wireless networks, IoT devices, sensor networks, telecommunications systems, distributed infrastructure, industrial systems, robotics, edge devices
- Experience with network telemetry such as: RSSI, SNR, packet loss, latency, link quality, routing, topology, network load, node availability, battery telemetry
- Graph & Network analytics
- Simulation & Synthetic data
- Forecasting & Reliability
- Experience with: degradation prediction, reliability modeling, predictive maintenance, capacity forecasting, anomaly detection, failure prediction, survival/lifecycle analysis
- Experience in telecommunications, IoT, industrial systems, robotics, defense or other physical-system domains would be a strong advantage.

Technical Skills:
Required
- Python
- NumPy
- Pandas
- scikit-learn
- SQL
- Git
- Jupyter / equivalent experimentation environment
Preferred
- PyTorch
- XGBoost / LightGBM
- Time-series libraries
- NetworkX
- MLflow or similar experiment tracking
- PyArrow / Parquet
- PostgreSQL / TimescaleDB or similar
- Cloud data/ML services

We are looking for a Senior AI / Machine Learning Engineer with a strong software engineering and backend development background.

Full-time | Remote | Long-term engagment

Start: ASAP

Candidate location: EU

This is a hands-on engineering role combining Machine Learning, LLMs, backend development, and MLOps. We are looking for someone who can take an AI/ML use case, build the required solution in Python, integrate it with existing services and workflows, and make sure it can be run, tested, and maintained in a production environment.

The ideal candidate has strong experience with Python and backend development, a solid understanding of Machine Learning, and practical experience working with LLM-based solutions. Experience with AWS is required, while knowledge of Docker, Kubernetes, and MLOps is important for working effectively with the existing engineering and platform teams.

Our current focus is increasingly moving towards LLM-powered solutions. We are particularly interested in engineers who have practical experience integrating LLMs into real applications and business workflows. Experience with technologies such as OpenAI, AWS Bedrock, or Google Gemini is highly relevant, as well as experience with RAG, embeddings, model adaptation, or fine-tuning.

At the same time, this is not a research-focused or prompt-engineering role. We need someone with strong engineering skills who can build the backend and internal services around AI capabilities. Experience with APIs, databases, asynchronous processing, authentication/authorization, and writing maintainable production code is important.

You should also have a good understanding of MLOps and the ML lifecycle, including how models and AI services are developed, tested, deployed, monitored, and maintained. Hands-on experience with Docker and basic Kubernetes knowledge is expected.

Deep DevOps expertise is not required. Our Application Platform Team is responsible for most of the infrastructure and production deployment. However, you should be comfortable working with cloud-based environments, understand the principles behind MLOps, and be able to independently run and test your solution locally using the appropriate development and containerization tools.

Experience with AWS is essential. Experience with services such as SageMaker is useful, particularly for traditional ML use cases, although the main focus of the team is now shifting towards LLM-based applications.

Experience building AI agents is not a requirement. What matters most is practical experience using LLMs as part of existing applications, internal services, and business processes.

Key requirements

Strong Python and software engineering skills

Significant backend development experience

Experience building APIs and internal services

Solid understanding of Machine Learning and ML model lifecycle

Practical experience with LLMs / Generative AI

Experience integrating LLMs into applications and workflows

Strong AWS experience

Understanding of MLOps

Experience with Docker and basic Kubernetes

Experience working with databases

Understanding of asynchronous programming and concurrency

Experience with authentication and authorization

Ability to run, deploy, and test solutions locally

Experience with OpenAI, AWS Bedrock, or Google Gemini

RAG and vector search

Embeddings

LLM evaluation and optimization

Fine-tuning / adapting foundation models

LangChain or LangGraph

MLflow, Kubeflow, SageMaker

Kafka, Redis, or similar distributed systems technologies

Location: Remote

Employment Type: Full-time

About the Role

We are looking for a Business Analyst with deep technical knowledge of Magento to bridge the gap between business needs and technical implementation.

In this role, you will work closely with stakeholders to understand business requirements and translate them into functional systems. You won't just document requirements — you will actively assist with setting up Magento configurations, guiding store setup, and ensuring the final solution perfectly aligns with the client’s operational and commercial goals.

Key Responsibilities

• Business Requirement Engineering: Gather, analyze, and translate complex business needs into clear technical specifications, user stories, and store setup scope.
• Hands-on Magento Configuration: Set up Magento admin panel configurations, configure native modules, tax rules, payment/shipping gateways, and store views to meet client specifications.
• Client Consultation & Solution Design: Advise clients on Magento’s native capabilities, best practices, and off-the-shelf vs. custom module tradeoffs to optimize their business processes.
• Functional Alignment: Act as the primary liaison between client stakeholders and dev teams to ensure technical solutions address underlying commercial goals.

• Validation & Support: Participate in acceptance testing, ensure solution accuracy against requirements, and assist clients during the launch phase.

   

Requirements & Qualifications

• Languages:
  • Fluent Polish (Professional working/conversational proficiency required for client communication).
  • Fluent English (Professional working/conversational proficiency required for internal & external collaboration).
• Magento Expertise: Strong technical and functional understanding of Magento (Adobe Commerce / Open Source), including key architecture concepts, core modules, data structures, admin panel capabilities, and configuration workflows.
• Business Orientation: Proven track record of translating real-world e-commerce requirements into structured Magento solutions.
• Hands-on Mindset: Capability to directly perform and oversee store configurations rather than just creating passive documentation.

• Communication: Exceptional stakeholder management and communication skills with both technical and non-technical audiences.

   

What We Offer

• 100% Remote Work: Flexibility to work from anywhere.
• Impactful Projects: Direct involvement in shaping scalable e-commerce platforms.
• Collaborative Environment: Work alongside senior developers and e-commerce experts in a streamlined, agile workflow.