Jobs ML / AI

About the role
We’re hiring a Senior AI Engineer to design, develop, and deploy enterprise AI solutions including Agentic AI, intelligent automation, and predictive analytics, aligned with governance/security frameworks and business objectives.

What you’ll do

• Build AI/ML models (classification, prediction, NLP, computer vision) and optimize for performance/cost 
• Build LLM-based solutions (chatbots, copilots, automation agents) 
• Design and implement multi-agent architectures, orchestration logic, decision flows, and human-in-the-loop controls 
• Own data prep, feature engineering pipelines, training/fine-tuning/evaluation, and model lifecycle/versioning 
• Integrate AI into enterprise systems via APIs/microservices, including real-time inference services 
• Implement responsible AI practices (bias control, explainability, auditability) and secure AI pipelines/APIs (auth/authz) 
• Monitor production performance, logging/alerting, drift, and optimize latency/scalability 

Must-have

• 5+ years AI/ML engineering; production deployments; enterprise-scale AI systems/APIs 
• Hands-on LLMs/NLP or Computer Vision 
• Strong Python ML stack (TensorFlow/PyTorch/Scikit-learn) 
• LLM ecosystem experience (Azure OpenAI / OpenAI / Gemini / HuggingFace), embeddings/vector DBs 
• Agentic/orchestration frameworks (LangChain / Semantic Kernel / AutoGen; multi-agent frameworks; tool use) 
• Azure AI Services/Azure ML/Databricks + Docker/Kubernetes; REST/microservices, event-driven, APIM 

Authority / constraints

• Owns AI architecture/model development standards and Agentic AI implementation approaches 
• Cannot deploy without security/governance approval; cannot use external AI services without compliance validation 

Nice-to-have certs

• AI-102 preferred; DP-100 recommended

Хелоу, люба спільното \0/

Ми продовжуємо нарощувати команду американського Survey SaaS, що аналізує поведінку юзерів під час digital опитувань. По суті, це платформа, де бізнес може засетапити своє опитування та отримати швидкий результат, очищений від ботів + отримати advanced behavioural analytics від нашої моделі.

Вам сподобається, якщо ви:
- самодостатні та организовані.
- хочете автономію та відповідальність за свою ділянку.
- вам дійсно цікаво те, чим ви займаєтесь.

На цьому я дозволю перемкнутись на англійську, бо далі конкретніше.

About the role

Build and own ML models that predict survey respondent certainty and detect fraud from behavioral signals — mouse movement, click patterns, timing, browser/OS data. You'll be the sole ML engineer maintaining a production hybrid CNN+tabular certainty model (already in client deliverables) and building the new bot detection model. You'll work directly with a US-based AI scientist who provides strategic direction.
 

What you'll work on

• Improve the hybrid 1D CNN (mouse trajectories) + MLP (tabular features) certainty model — error analysis, hyperparameter tuning, augmentation, loss experiments 
• Build the bot detection classifier — binary human/bot model reusing behavioral pipeline infrastructure 
• Run segment-level evaluations — per-survey quality checks, precision/recall optimization, calibration analysis 
• Track experiments in MLflow — every run logged with parameters, metrics, dataset versions 
• Process raw time-series — mouse trajectories as [T, 7] tensors (x, y, speed, acceleration, dt, dx, dy) 
• Document to audit standard — code, dataframes, notebooks, reproducible results with artifact links
   

What we're looking for

• Strong PyTorch — built and trained CNNs from scratch, not just fine-tuned 
• Tabular ML experience — LightGBM, XGBoost, or gradient boosted trees 
• Rigorous evaluation mindset — calibration, segment analysis, label quality, beyond single-metric optimization 
• Time-series/sequence data experience — temporal patterns in any domain (NLP, audio, sensors, clickstream) 
• Imbalanced classification expertise — precision/recall tradeoffs, threshold tuning, AUC-ROC 
• MLflow or similar (W&B, Neptune) 
• Self-directed — you're the only ML person on execution, strategic guidance from US AI scientist 
• Auditable work — advisor reviews regularly, notebooks/scripts must be clean
• Clean verbal English
   

Nice to have

Behavioral/clickstream analysis • time-series augmentation • fraud/anomaly detection • model serving • survey methodology/psychometrics
 

Stack

Python • PyTorch • LightGBM • MLflow • HDF5 • PostgreSQL
 

What this is NOT LLM/GenAI role, but classical ML + deep learning on structured behavioral data 

The Team

• Product Manager
• 2x Fullstack Devs
• Anti-Fraud Automation QA
• DevOps
   

We offer

• 15 paid day-offs per year.
• 10 US/UA holidays.
• Flexible schedule and fully remote work from anywhere with reliable overlap to CET.
• Senior team members and strong AI Scientist Advisor.
• Minimal bureaucracy, high authonomy and trust, fast decisions.

Responsibilities

• Refactor a single-agent prototype into a multi-agent architecture (orchestrator + specialized agents).
• Design orchestration logic, agent communication, and state/session management.
• Implement guardrails (prompt-injection protection, output validation).
• Integrate external APIs with resilient error handling.
• Optimize performance and latency (<2–4 seconds).
• Implement monitoring, telemetry, and observability for production systems.
• Collaborate with cross-functional teams to ensure robust API design and integration.

Job requirements

• Strong experience with LLM systems and multi-agent architectures.
• Proficient in Python, including FastAPI, async programming, and Pydantic.
• Experience with function calling and JSON schema outputs.
• Strong API design skills and integration experience.
• Experience in Azure environment: AI Foundry, Key Vault, Managed Identity, monitoring.
• Solid experience in system architecture and production deployment.
• English: conversational; German: A2 comprehension.

Nice to Have:

• MCP certification or relevant Azure certifications.

This is not a coding role. This is a systems-of-systems role. 

If you build things with AI, this posting isn't for you. 

If you build the systems that govern how AI builds things — keep reading.

What This Role Is

LifeChef is a HIPAA-compliant healthcare platform delivering Medically Tailored Meals. Our engineering team builds on a methodology called Enso — a spec-driven, AI-augmented development framework where every feature starts with a specification, gets built with AI assistance, and is validated against defined criteria.

Enso v1 is linear: spec → generate → validate → ship. It works. But it's hitting scaling limits — the document graph grows with every feature, validation complexity compounds, and the process requires increasing human oversight to maintain quality. This is a familiar problem in any AI-assisted workflow that outgrows its initial architecture.

We need someone to take Enso from v1 to v2: a true agentic system where specialized AI agents handle research, specification validation, code generation oversight, quality assurance, and deviation detection — with structured autonomy, clear escalation paths, and human-in-the-loop at defined decision points, not everywhere.

You would partner directly with the CTO (who designed the current methodology and has strong opinions about where it needs to go) to architect, build, and maintain this system. The first 30 days are heavy collaboration — translating vision into working architecture. After that, you own the evolution of the process while the CTO focuses on product and team.

What You'd Actually Build and Maintain

• Agent topology for the development lifecycle: research agents, spec validation agents, code review agents, compliance checking agents, red-team/adversarial agents — each with defined boundaries, confidence thresholds, and escalation triggers
• Spec-to-code reconciliation system: agents that validate whether implemented code actually satisfies the specification, flagging deviations and ambiguities rather than silently passing
• Multi-model orchestration strategy: routing different tasks to different models based on cost, capability, and risk (expensive models for reasoning, cheap models for classification, open-source where it fits)
• Structured output validation: ensuring agent outputs are parseable, auditable, and actionable — not just "LLM said it's fine"
• Governance and escalation framework: what agents decide autonomously vs. what requires human sign-off, circuit breakers for low-confidence situations, audit logging for compliance
• Evolution engine: the system should improve itself — tracking where agents fail, where human overrides happen, and feeding that back into prompt refinement and architecture changes
• Integration with existing CI/CD: the agentic layer hooks into GitHub Actions, automated testing (Vitest), PHI scanning, secret detection, and compliance validation already in the pipeline

This is infrastructure that the development team operates within. You're not shipping product features — you're shipping the machine that makes product development faster, safer, and more consistent.

What We Need From You

Hard requirements:

• You have designed or built at least one agentic system where AI agents operate with structured autonomy — multi-step workflows with validation, error handling, and escalation. Not just API wrappers. Not just chatbots.
• You understand agent topology as a design problem: which agents need to exist, what their boundaries are, how they communicate, how you prevent cascading failures.
• You have hands-on experience with LLM orchestration frameworks (LangChain, CrewAI, AutoGen, custom implementations — the specific tool matters less than the depth of understanding).
• You think in terms of eval frameworks and validation: how do you know if an agent's output is good? How do you measure drift? How do you build confidence scoring that's actually useful?
• You understand multi-model strategies: when to use which model, cost/capability tradeoffs, how to tier your model usage so you're not burning expensive tokens on classification tasks.
• You can work in both Python and TypeScript, or at minimum are comfortable in one and willing to operate in the other. Our product stack is TypeScript-first but agentic tooling often lives in Python.
• Strong written communication. This role involves heavy async documentation of architectural decisions, process design, and system behavior.

What sets you apart:

• Experience with Model Context Protocol (MCP) — building custom MCP servers, tool-use architectures
• Claude Code, Cursor, or similar AI-native development tools as part of your own workflow (you should be using what you're building systems around)
• Experience in healthcare, fintech, or regulated environments where AI outputs must be auditable and compliant
• Understanding of prompt engineering at the systems level — not "write a good prompt" but "design a prompt architecture that's maintainable, versionable, and testable"
• You've thought seriously about adversarial validation — red-team agents, cross-model verification, structured disagreement as a quality signal
• You have opinions about where agentic AI is overhyped and where it genuinely works, and you can defend those opinions with specifics

What we explicitly don't require:

• A portfolio of 50 agentic projects. The field is young. One deep project with real orchestration depth beats ten shallow ones.
• Product development skills. You won't be building UI or shipping user-facing features.
• Healthcare domain expertise. We'll teach you the domain. You teach us the orchestration.

How We Evaluate

1. Screening questions (in your application) — We're looking for specificity and honesty, not keyword density.
2. Architecture conversation with CTO (90 min) — We'll describe our current system, its limitations, and where we want to go. You'll tell us how you'd approach it. This is a two-way design conversation, not a quiz.
3. Small design exercise (async, take-home) — Given a simplified version of our spec validation problem, design an agent topology. We're evaluating how you think about boundaries, failure modes, and escalation — not production-ready code.
4. Trial sprint (60–90 days) — Heavy pairing with CTO in the first 30 days, increasing autonomy after. Defined milestones at 30, 60, and 90 days.

The Team and Context

Small, senior engineering team, primarily Ukraine-based. The CTO (Jay) designed the Enso methodology, has 20+ years of engineering experience across healthcare, threat intelligence, and enterprise platforms, and has built the governance architecture (constitutions, spec-gates, validation layers) that you'd be evolving. He's technical, direct, and has strong opinions — but he's hiring you because he needs a partner in this, not an executor.

The product stack is Next.js 16, React 19, TypeScript, PostgreSQL, AWS serverless (Lambda, CDK, Amplify Gen 2), with Vitest testing, Pino structured logging, and HIPAA compliance enforced in CI. You don't need to know this stack deeply, but you need to understand it well enough to build agentic tooling that integrates with it.

We're building toward autonomous agentic systems for clinical operations — this development process work is the foundation that makes that possible. If the idea of designing the system that governs how an engineering team builds software excites you, this is the role.

How to Apply

Reply with:

• The most complex agentic or LLM orchestration system you've built — what it did, how it was structured, what you learned
• Your honest take on where agentic AI is right now and what's actually hard
• What interests you about building process infrastructure vs. product features

Generic responses get skipped. We know the field is young — we'd rather hear about one deep experience than a list of tools you've touched.

About Atlas Technica & our AI & Advisory Practice 

Atlas Technica is the premier technology partner for the world’s most sophisticated hedge funds, private equity firms, and RIAs. We power the infrastructure behind the scenes, and now we are building the "factory floor" for their next generation of alpha. We are evolving into the industry’s first Managed Intelligence Provider (MIP). 

Our AI & Advisory practice doesn't just sell licenses. We architect the secure blueprints and complex agentic frameworks that allow elite investment teams to operate at the frontier of GenAI. We are currently scaling a product lane centered on Intelligent Workflow Design: a tiered, institutional offering designed to rewire how fundamental research and investment operations happen. 

The Mission 

We are looking for a Principal, Agentic Strategy & Solutions to architect and pilot our client-facing advisory business. This is a strategic player-coach role for an elite practitioner who is tired of making slides and wants to actually build the future of high finance. 

You will start as our lead architect for client enablement, personally designing the multi-agent systems that drive real ROI for our clients. As the practice scales, you will have the mandate to hire and lead a specialized pod of 2–3 AI consultants, initially, ensuring our global delivery model remains as sharp as our code. 

What You’ll Own 

1. Strategic Product Development 

• Architect the Atlas "Agentic Workflow Training" series as a repeatable, institutional product. We want intensive cohorts and strategic office hours, not generic webinars. 
• Develop and maintain proprietary Pattern Libraries for complex fund strategies: 
• Long/Short Equity: Automating fundamental synthesis and 10-K/Q anomaly detection. 
• Macro / Multi-Strat: Building global news triage systems and cross-asset scenario companions. 
• Credit & Private Markets: Engineering covenant extraction and automated deal-memo synthesis. 
• Define the "Basic" vs "Advanced" AI Factory playbooks, ensuring we have a solution for every stage of client maturity. 

2. Hands-On Agentic Orchestration 

• Design sophisticated system prompts and multi-agent workflows.  
• Work daily in the "workbench": utilizing Claude, ChatGPT, … to ship production-ready patterns. 
• Establish robust evaluation harnesses to measure agent performance, hallucination risk, and research depth. You find a 1% error rate on a financial metric personally offensive. 

3. Executive Advisory & Client Delivery 

• Lead high-stakes design sprints for C-suite stakeholders at $10B+ AUM funds. 
• Partner with Sales and CTMs to translate "we want AI" into scoped, high-value consulting engagements that deliver. 

4. Internal Practice Enablement 

• Serve as the firm-wide "Internal Coach" for prompt and agent fluency. You’ll create the playbooks that empower our engineering teams to sustain these environments. 
• Contribute to R&D on "AI Defense," focusing on prompt safety and governance patterns for SEC and FINRA-regulated environments. 

Your DNA 

• The Consulting Pedigree: You have 7+ years of experience within a global consultancy (e.g., EY, Deloitte, Accenture, McKinsey, BCG) in a client-facing, engagement-leadership role. You know how to manage a room and a project plan. 
• The Builder Mindset: You are a daily practitioner of LLMs and orchestration frameworks. You don't just "manage" the tech; you operate in the terminal and understand what works. 
• Capital Markets Fluency: You speak the language of PMs, Research Analysts, and CCOs and understand the fundamental research process. 
• The Clever Edge: you can demystify "Retrieval Augmented Generation" for a non-technical COO while keeping the technical respect of a Principal Architect. 
• High-Velocity Orientation: You thrive in the "N of 1" environment where the tech changes weekly. You believe that shipping beat perfection every single time. 

What Success Looks Like (First 12 Months) 

• Q1-Q2: A codified training offer with a validated library of "Bronze" workflow families ready for global scale. 
• Q3-Q4: High NPS and measurable research compression for a cohort of pilot clients. 
• Practice Growth: A clear roadmap and your first 2–3 direct reports hired as practice revenue hits target milestones. 

Our Customer:

Our client is a technology-focused company building high-performance, real-time ML inference systems. The team develops ultra-low-latency engines that process billions of requests per day, integrating ML models with business-critical decision-making pipelines. They are looking for an experienced backend engineer to own and scale production-grade ML services with strong focus on latency, reliability, and observability.

Your tasks:

• Lead the design and development of low-latency ML inference services handling massive request volumes.
• Build and scale real-time decision-making engines, integrating ML models with business logic under strict SLAs.
• Collaborate closely with data scientists to deploy ML models seamlessly and reliably in production.
• Design systems for model versioning, shadowing, and A/B testing at runtime.
• Ensure high availability, scalability, and observability of production systems.
• Continuously optimize latency, throughput, and cost-efficiency using modern tools and techniques.
• Work independently while collaborating with cross-functional teams including Algo, Infrastructure, Product, Engineering, and Business stakeholders.

Required Experience and Skills:

• B.Sc. or M.Sc. in Computer Science, Software Engineering, or related technical field.
• 5+ years of experience building high-performance backend or ML inference systems.
• Expert in Python and experience with low-latency APIs and real-time serving frameworks (e.g., FastAPI, Triton Inference Server, TorchServe, BentoML).
• Experience with scalable service architectures, message queues (Kafka, Pub/Sub), and asynchronous processing.
• Strong understanding of model deployment, online/offline feature parity, and real-time monitoring.
• Experience with cloud environments (AWS, GCP, OCI) and container orchestration (Kubernetes).
• Familiarity with in-memory and NoSQL databases (Aerospike, Redis, Bigtable) for ultra-fast data access.
• Experience with observability stacks (Prometheus, Grafana, OpenTelemetry) and alerting/diagnostics best practices.
• Strong ownership mindset and ability to deliver solutions end-to-end.
• Passion for performance, clean architecture, and impactful systems.

Would be a plus:

• Prior experience leading high-throughput, low-latency ML systems in production.
• Knowledge of real-time feature pipelines and streaming data platforms.
• Familiarity with advanced monitoring and profiling techniques for ML services.

This is a fantastic opportunity to join a fast-growing company at the heart of the digital revolution. Our software product is revolutionising manufacturing through automation. We are trusted by global brands and work across sectors (mobility, consumer goods, machinery etc).

We have a talented and ambitious team. We value integrity and are passionate about our mission to revolutionise advanced manufacturing.

This is your opportunity to:

• Be part of a fast-growing software product company in advanced manufacturing.
• Learn and fulfil your potential in an open, knowledge-driven culture.
• Share in our ambitious vision and make a meaningful difference towards autonomous manufacturing.

Responsibilities: 

• Develop, test, and deploy generative AI based solutions.
• Design and implement end-to-end AI solutions that integrate with existing systems. 
• Collaborate with software engineers and product teams to understand business needs and translate them into technical solutions. 
• Research emerging AI trends and techniques to continually improve the AI solution offerings. 
• Prepare and maintain documentation on design, code, and processes to ensure organizational knowledge retention. 
• Monitor and evaluate the performance of AI systems in production, identifying areas for improvement or further development.

You will be joining a well-versed team if you have:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. 
• Proven experience in developing generative AI or machine learning models.
• Commercial experience in using large language models.
• Programming skills in languages such as Python or JavaScript.
• Familiarity with databases (SQL, NoSQL). 
• Solid understanding of deep learning, NLP, computer vision, or other specialized areas within AI is a plus. 
• Experience with cloud platforms (AWS, Google Cloud, Azure) and containerization (Docker, Kubernetes) is beneficial. 
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
• Good communication skills and the ability to translate complex technical concepts to non-technical stakeholders. 
• Commitment to continuous learning and development in the field of AI and emerging technologies.

About the Role

We are looking for an experienced Voice AI Agents Engineer who will be responsible for building, testing, and improving AI voice agents used in our products and internal tools.

This role focuses specifically on the development of Voice AI agents — systems that can understand speech, process user intent, and respond naturally using modern AI models.

You will work closely with the product and engineering teams to design reliable and scalable voice interactions, experiment with new technologies, and continuously improve the performance of our AI agents.

Responsibilities

• Design, build and maintain Voice AI agents
• Develop pipelines for speech-to-text, LLM processing, and text-to-speech
• Test and improve conversation flows and voice interaction quality
• Integrate voice agents with internal systems, APIs, and business tools
• Monitor performance and optimize response quality, latency, and reliability
• Experiment with new AI models and voice technologies
• Work closely with product managers and developers to ship AI-driven features

Requirements

• 1.5+ years of hands-on experience building Voice AI agents
• Strong experience with LLMs and AI agent architectures
• Experience working with speech-to-text and text-to-speech systems
• Solid programming skills (Python or Node.js)
• Experience integrating AI systems with APIs and external services
• Understanding of conversational AI design and dialogue systems
• Ability to independently design and implement AI-driven solutions

Nice to Have

• Experience with telephony or real-time voice systems
• Experience with frameworks for building AI agents
• Experience with prompt engineering and evaluation pipelines
• Experience with cloud platforms (AWS, GCP, or similar)

What we offer

• Fully remote work format and flexible working hours.
• Competitive salary pegged to USD.
• 24 days of paid vacation (sick leave, national, religious, and company holidays).
• A friendly communication culture and transparent processes.
• Dynamic work environment.
• Opportunities for professional development and career growth.
• No micromanagement — just trust, ownership, and freedom to experiment.

We are looking for a Senior Data Scientist with strong technical expertise and a practical mindset to work on complex data-driven challenges. In this role, you will design and implement ML and GenAI solutions, experiment with modern approaches, and transform ambiguous business problems into scalable, production-ready systems.

Requirements
Must-have

• 4+ years of experience in Data Science
• 4+ years of hands-on experience with Python
• Strong understanding of Machine Learning fundamentals, including:
  • regression and classification models
  • feature engineering and model evaluation
• Practical experience with LLMs and GenAI, including:
  • GPT / ChatGPT or similar LLMs
  • LangChain
  • Retrieval-Augmented Generation (RAG)
• Experience with EDA, data preprocessing, and basic dimensionality reduction (e.g. PCA)
• Hands-on experience with tree-based models (e.g. XGBoost)
• Experience building scalable and high-load solutions
• Ability to write clean, maintainable code
• Participation in technical and architectural decisions
  
  Nice to have
• Experience with NLP libraries (SpaCy, Hugging Face)
• Understanding of Explainable AI (XAI) and model interpretability
• Experience with cloud platforms (AWS / GCP / Azure)
• Ability to translate business problems into ML solutions

• Background in R&D, experimentation, or prototyping

  
  Key Responsibilities

• Develop AI agents that utilize LLM, RAG and langchain approach;
• Implement LLM and Diffusion models to boost business productivity;
• Utilize LLM (LLM Vision) to improve object detection, text classification and extraction;
• Create forecasting, recommendation, and classification models;
• Transform business challenges to AI applications;
• Conduct R&D activities: explore new ML/GenAI techniques and assess their practical impact.
• Collaborate with engineering, product, and business teams to deliver impactful data-driven solutions.
  
  We offer:
• Great networking opportunities with international clients, challenging tasks;
• Building interesting projects from scratch using new technologies;
• Personal and professional development opportunities;
• Competitive salary nominated in USD;
• Paid vacation and sick leaves;
• Flexible work schedule;
• Friendly working environment with minimal hierarchy;

• Team building activities, corporate events.

   

As an ML Engineer, you'll work at the intersection of machine learning systems, knowledge representation, and reasoning infrastructure — building the foundational intelligence layer that powers our platform. This isn't about wrapping APIs or fine-tuning models in isolation. You'll be solving hard problems at the frontier of applied ML.

What you'll work on:

Designing ML pipelines that transform unstructured, proprietary information into semantically rich, queryable representations

Building and integrating models that enable probabilistic reasoning, multi-hop inference, and context-aware decision support

Developing agentic workflows where AI assists human judgment through explainable outputs, uncertainty quantification, and iterative feedback loops

Defining evaluation metrics and testing frameworks for high-stakes, information-led environments

Contributing to architectural decisions around ML infrastructure and model serving

Collaborating closely with backend engineers, product, and domain experts

What We're Looking For

Strong foundations in machine learning — NLP, information extraction, representation learning

Systems thinking mindset, not just model performance in isolation

Comfortable working in ambiguity and defining problems from first principles

Intellectually honest — willing to challenge assumptions, including our own

Pragmatic: ships working systems, iterates on real feedback, avoids over-engineering

Cares about reproducibility, clarity, and long-term maintainability

What We Offer

Work on genuinely hard, foundational problems at the intersection of ML and knowledge systems

Founder-led, deeply technical environment — low-ego, high-ownership

Fast feedback loops, strong opinions, high bar for clarity

Flexible/hybrid setup centred around London

Early-stage equity

Our Values

First-principles thinking. Human judgment matters. Intellectual honesty. Trust by default. Compounding advantage. Build infrastructure, not wrappers.

Responsibilities:

• Own and support core ML infrastructure (MLflow, CVAT, GPU/CPU cloud instances, internal data tools).
• Assist Data Engineers with deploying and maintaining data pipelines.
• Act as the primary contact for ML and Data teams on infra-related requests.
• Improve reliability and efficiency by adding automation, monitoring, and tooling enhancements.
   

Must have:

• Knowledge of Python, Terraform, Docker, Linux.
• Hands-on experience with cloud environments (Azure prefered).
• Understanding of the end-to-end ML lifecycle (data → training → evaluation → deployment → monitoring).

Nice to have:

• Familiarity with Kubernetes.
• Experience with FFmpeg or GStreamer for video processing and streaming.
• Experience with model serving frameworks and inference formats (ONNX, TensorRT, OpenVINO, NVIDIA Triton).
• Experience deploying computer vision models, especially to edge/embedded devices (NVIDIA Jetson, Intel platforms, Raspberry Pi).

• Надаємо бронювання від мобілізації

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.

Join our dynamic AI team and be at the forefront of innovation, working on transformative projects for some of the most recognized companies worldwide. As an AI Consultant, you will participate in the implementation and development of AI-powered solutions across a broad range of applications, from Gen AI tools to data analytics fraud detection systems.

Key Responsibilities:

• Participate in AI projects from inception to completion, ensuring timely delivery of high-quality solutions;
• Solution Development: Design and implement cutting-edge AI and GenAI solutions, including Retrieval-Augmented Generation (RAG) for knowledge retrieval, prompt optimization, and fine-tuning of large language models (LLMs);
• Apply AI techniques to areas such as fraud detection, satellite imagery analysis, time-series forecasting, and automated data quality validation to enhance decision-making and automation;
• Develop web applications (FE / BE / cloud architecture) enabling users to interact with AI systems;
• Client Interaction: Work closely with clients to understand their unique needs and provide expert guidance and support throughout the project lifecycle.

Requirements and skills:

• 2+ years of professional experience in this or similar area;
• Experience working on AI/ML projects, in areas such as Generative AI, NLP, Computer Vision or Machine Learning model development and deployment;
• University degree in Mathematics or a similar field is an advantage;
• Programming skills in Python (pandas, plotly, numpy);
• Experience with Full stack development (JavaScript) is a significant plus.
• Experience with SQL databases; familiarity with NoSQL is a plus;
• Experience with generative AI models is a plus;
• Fluent in English (level B2 or higher).

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

We are looking for AI-driven Software Engineer to join our long-term project.

Responsibilities:

• Design practical, scalable software architectures
• Translate business requirements into clear technical solutions
• Define data models, APIs, integrations, and system structures
• Contribute across the full lifecycle — from idea to production

You’ll be part of a small, fast-moving team where ownership and initiative are highly valued.
 

Who you are

• You take ownership and enjoy responsibility
• You communicate clearly with both technical and non-technical stakeholders
• You’re comfortable in a fast-paced, evolving environment

You also:

• Think in systems and trade-offs, not rigid rules
• Take a hands-on, proactive approach
• Balance structure with pragmatism
• Stay curious and keep up with new technologies
• See AI as a practical tool, not just a trend
   

Requirements:

You don’t need to master everything, but you’re familiar with or interested in:

• APIs and system integrations
• Modern architectures (React, React Native, TypeScript, Next.js, Supabase)
• AI-assisted development tools (e.g., Cursor, Claude Code)
• Automation tools (e.g., n8n)
• No-/low-code platforms (e.g., Bubble.io — a plus)
  
  Location Belgium must have!
   

👉 What matters most: understanding core principles, not just tools.

We are currently looking for an AI Pro Code Developer. This is an outstaff position, where you will work as part of the client’s engineering team on building and scaling modern AI-driven data infrastructure.
 

Technology Stack & Requirements

• AI & Cloud Architecture:
  End-to-end design and ownership of AI solutions using Microsoft Azure, including Azure OpenAI Service, Azure Data Lake, and related data/AI services
• Cloud Governance & Security:
  Strong alignment with enterprise IT standards, including security, compliance (e.g., GDPR, PCI where applicable), identity & access management, and cloud governance frameworks
• DevOps & Infrastructure:
  Definition and implementation of DevOps strategy, including:
  • CI/CD pipelines (e.g., Azure DevOps, GitHub Actions)
  • Infrastructure as Code (IaC) (e.g., Terraform, ARM/Bicep)
  • Monitoring & observability (e.g., Azure Monitor, Application Insights, logging frameworks)
• Software Engineering:
  Hands-on development and technical leadership using:
  • .NET / C# for backend services and APIs
  • Python for AI/ML integration, data processing, and automation
• Scalability & System Design:
  Experience designing systems for high scalability, performance, and maintainability, including microservices, distributed systems, and data pipelines
• Stakeholder Collaboration:
  Ability to work closely with customer engineering and IT teams, acting as a technical lead to ensure successful delivery, integration, and long-term sustainability of solutions
  
  We Offer
• ✔️ Full-remote work
• 🏝 18 vacation days per year
• 🎁 Birthday celebration present
• 🧑🏻‍💻 Learning and development opportunities

Trinetix is looking for a skilled AI Engineer. 

Join our team at Trinetix and collaborate on a project with a global telecommunications leader operating one of the world’s most reliable Internet backbones across more than 100 countries. In this role, you will contribute to large-scale AI initiatives focused on improving network operations through intelligent automation, predictive insights, and AI-driven workflows. 

You will work on GenAI applications, AIOps intelligence, and RAG-based systems, helping to transform how complex telecom infrastructure is monitored, analyzed, and operated. The role combines engineering, data, and product thinking to deliver AI solutions with measurable business impact. 

Join us and grow your career in a dynamic international environment while helping shape the future of AI-driven network operations. 

What You’ll Do:

• Design and implement AI/ML pipelines, including LLM-based applications, GenAI frameworks, and vector retrieval systems. 
• Build production-grade AI integrations in Python, ensuring scalability and reliability. 
• Apply enterprise AI patterns such as RAG, observability, agent workflows, and model evaluation. 
• Architect secure, scalable integrations between cloud platforms and enterprise systems. 
• Develop data pipelines, event streams, and observability frameworks to support AI at scale. 
• Collaborate with Data Scientists on feature engineering, model evaluation, and experiment design. 
• Contribute to AI-driven telecom solutions by learning and applying concepts in IP routing, EVPN/L3VPN, telemetry, and optical networking. 
• Translate technical capabilities into measurable business impact through KPIs, ROI analysis, and product-focused thinking. 

About You:

• 5+ years of experience in data, analytics, machine learning, or AI-driven environments. 
• Strong hands-on expertise with LLMs, GenAI frameworks, ML/analytics pipelines, and vector databases. 
• Proficiency in Python and experience delivering production-grade AI solutions. 
• Solid understanding of enterprise AI patterns (RAG, observability, agent workflows, model evaluation). 
• Knowledge of data pipelines, event-driven architectures, and system-to-system integration. 
• Familiarity with data science practices: feature engineering, statistical modeling, experiment design, and hypothesis-driven analysis. 
• Ability to work with structured and unstructured data, conduct exploratory analysis, and guide AI solution design. 
• Experience collaborating across engineering, architecture, IT, and operations teams. 
• Strong communication skills to simplify AI concepts for non-experts. 
• Curiosity and adaptability to learn emerging AI tools and telecom domain concepts. 

Must-haves:

• AI/ML Engineering: LLMs, GenAI frameworks, vector retrieval systems, ML lifecycle practices. 
• Programming: Python (production-grade AI integrations). 
• Architecture & Integration: Cloud-native integrations, event streams, observability, secure connectivity patterns. 
• Data Science Collaboration: Feature engineering, model evaluation, experiment design. 
• Telecom Learning Ability: Motivation to learn IP routing, EVPN/L3VPN, telemetry, optical networking. 
• Product Thinking: KPI design, ROI measurement, business impact translation. 

Ideally, You Also Have:

• Experience with RAG-based systems and advanced observability frameworks. 
• Familiarity with MLOps practices (model monitoring, validation, lifecycle management). 
• Knowledge of agent workflows and orchestration patterns in enterprise AI. 
• Exposure to telecom-specific AI use cases (network telemetry, predictive maintenance, intelligent automation). 
• Сloud-native AI platforms and scalable data infrastructure. 

What we offer:

• Continuous learning and career growth opportunities 
• Professional training and English/Spanish language classes   
• Comprehensive medical insurance 
• Mental health support 
• Specialized benefits program with compensation for fitness activities, hobbies, pet care, and more 
• Flexible working hours 
• Inclusive and supportive culture