Jobs ML / AI

Role overview:

We are looking for a practical security engineer who can improve detection quality, investigate real incidents, and help reduce manual SOC work.

This role is not about building an “AI security platform”. The focus is on better visibility, cleaner detections, faster investigations, and useful automation.

What You Will Work On:

Threat Hunting

Run targeted hunts based on clear hypotheses, including:

• Suspicious cloud activity
• Abnormal IAM or API usage
• Unusual network or application behavior
• Weak or missing detection coverage
• Gaps in logging and telemetry

You will use internal data and external threat intelligence to validate risks and improve detection logic.

Security Incident Investigation

Investigate security alerts and incidents end-to-end:

• Understand what happened
• Validate whether the alert is real or noisy
• Identify affected users, systems, and data
• Collect evidence from logs and infrastructure
• Document findings clearly
• Recommend fixes or detection improvements

Expected experience: 2+ years of hands-on security incident investigation.

Detection Engineering

Improve existing detections and create new ones where needed:

• Reduce false positives
• Improve signal quality
• Tune alerts based on real data
• Validate whether detections catch meaningful activity
• Map useful detections to MITRE ATT&CK where appropriate.

Automation and Internal Tooling

Build small but useful tools to reduce repetitive work:

• Scripts for log analysis
• Alert enrichment
• Investigation helpers
• Data normalization
• Tool integrations
• Basic SOAR-style automation

Practical AI / LLM Usage

Use LLMs only where they bring practical value:

• Alert enrichment
• Investigation summaries
• Internal tooling
• Analyst productivity

No hype. No AI-first positioning. AI is just one tool in the workflow.

Requirements:

• Solid experience with SIEM tools, such as: Elastic, Splunk, Microsoft Sentinel, Similar platforms
• 2+ years investigating real security incidents
• Strong understanding of: - AWS logs and IAM activity - Cloud security basics - Network fundamentals - APIs, authentication flows, and modern application behavior
• Comfortable working with: Large datasets; Raw logs; Noisy alerts; Incomplete telemetry
• Python or similar language for data processing
• Experience with automation: SOAR, Scripts, Internal tools, Custom workflows
• Basic DevOps knowledge: - AWS - Linux - Docker - Kubernetes basics - CI/CD basics - Cloud infrastructure concepts - Logs, metrics, and monitoring.

Nice-to-have (but not critical):

• Experience applying simple ML or statistical methods to logs
• Familiarity with LLMs in internal tooling
• Experience with data pipelines (Kafka, Spark, Similar systems)
• MITRE ATT&CK mapping
• Detection-as-code experience
• Understanding of infrastructure monitoring (Prometheus, Grafana, OpenTelemetry)

What will make you effective here:

• You do not trust alerts blindly
• You care about signal quality, not the number of detections
• You can explain why a detection is noisy or useless
• You understand systems, not just security tools
• You are comfortable reading raw logs
• You prefer building automation once instead of repeating manual work
• You can work with engineers and explain security findings in practical terms

Responsibilities:
• Work closely with different departments (Marketing, Sales, HR, Finance, etc.) to understand their workflows and identify opportunities to improve efficiency using AI tools.
• Analyze existing processes and propose automation or AI-assisted solutions.
• Implement AI-powered automations using available tools (LLMs, workflow automation platforms, data tools, etc.).
• Build and maintain internal AI workflows, prompts, and integrations.
• Train and support team members in using AI tools in their daily work.
• Monitor performance of implemented automations and continuously improve them.
• Document AI workflows, best practices, and internal guidelines.

Requirements:
• Strong curiosity about AI and automation and motivation to continuously learn.
• Basic understanding of AI tools (e.g. LLMs, automation platforms, data tools) and how they can be applied in business processes.
• Analytical mindset and ability to break down workflows and identify inefficiencies.
• Basic technical skills (e.g. working with APIs, automation tools, spreadsheets, or scripting is a plus).
• Experience in operations, data analysis, product, or technical roles is a plus.
• Ability to communicate with both technical and non-technical teams.
• Problem-solving mindset and willingness to experiment with new tools.
• Self-driven and proactive approach to identifying improvement opportunities.

Nice to Have:

• Experience with automation tools (Zapier, Make, n8n, etc.).
• Experience with prompt engineering and LLM-based workflows.
• Basic knowledge of Python, SQL, or data analysis.
• Experience implementing internal tools or process improvements.

We offer:
• Convenient work schedule (8/9/10/11 — 17/18/19/20);
• 21 working days of vacation;
• 10 working days of sick leave;
• Extra days off for birthday, wedding, etc and for public holidays;
• Payment for training;
• Financial and intangible gifts from the company;
• Free communication, the ability to influence processes and implement your ideas;
• Comfortable and easy management, no tyranny.

How We Hire:
We’re not here to make you jump through flaming hoops (unless that’s your hobby — in which case, we’ll cheer you on). Our goal is to see if we’re a good match for each other — and to make sure you enjoy the ride.

1. Pre-Screen Call — 30 min
A quick hello with one of our recruiters. We’ll talk about your background, your goals, and what you’re looking for. In return, we’ll tell you more about ABXplay, the role, the team, and answer any burning questions.
2. Hard Skills Interview — 30–60 min
You’ll meet your potential manager. We’ll dive into the real stuff — your future responsibilities, the challenges ahead, and the kind of work you’d actually be doing (no mystery here).
3. The Offer Stage

We’re focusing on:

• Complex document processing pipelines
• Knowledge retrieval systems powered by LLMs and RAG
• Building production-ready GenAI solutions with real business value

I’m open to candidates of different experience levels — what matters most is strong problem-solving skills, curiosity, and hands-on experience.

You should have:

• Solid understanding of machine learning and NLP algorithms
• Practical knowledge of how LLMs and Retrieval-Augmented Generation (RAG) work

• Previous experience with real-life AI/ML projects (commercial) - at least 1 year

   

Atlas Technica’s mission is to shoulder IT management, user support, and cybersecurity for our clients, who are hedge funds and other investment firms. Founded in 2016, we have grown 100% year over year through our uncompromising focus on service.

We value ownership, execution, growth, intelligence, and camaraderie. We are looking for people who share our Core Values, thrive, and contribute to this environment while putting the customer first. At Atlas Technica, we offer a competitive salary, comprehensive benefits, and great perks to our global Team. We strive to maintain a professional yet friendly environment while promoting professional and career development for our Team Members. Join Atlas Technica now!

Design and deploy intelligent chatbots using Foundry Workflows, Microsoft Agent Framework, OpenAI. Build and operate multi-agent orchestration pipelines with Azure AI Foundry and Model Context Protocol (MCP). Integrate AI with enterprise systems via RESTful APIs, ensuring compliance with security and privacy standards. Stay current with LLM advancements, collaborate across teams, and mentor junior engineers.

Responsibilities:

AI Agent & Chatbot Development

• Design, build, and deploy intelligent conversational agents using Foundry Workflows, Microsoft Agent Framework, OpenAI.
• Develop prompt strategies and manage datasets to support context-aware, multi-turn dialogue and accurate responses.
• Conduct rapid prototyping to validate technical feasibility and demonstrate business value of AI-driven solutions.
• Design agentic loops with task decomposition, state passing between agents, and workflow enforcement for multi-step ticket resolution.

• Implement structured error responses and graceful degradation in agent tool calls, including timeout handling, retry logic, and fallback cascades.

   

Data & Model Engineering

• Build and maintain data ingestion and indexing pipelines for Azure AI Search and Cosmos DB.

• Evaluate and optimize AI system performance for accuracy, scalability, latency, and cost-efficiency.

   

System Integration & Architecture

• Architect and implement integration between AI and enterprise systems (e.g., ConnectWise PSA, Confluence, etc.) using RESTful APIs.
• Implement and maintain MCP servers (Model Context Protocol) for secure, typed tool integration between AI agents and enterprise APIs.
• Deploy containerized services to Azure using multi-stage builds and Azure Pipelines CI/CD.
• Security, Compliance & Governance
• Ensure AI systems comply with data privacy regulations (e.g., GDPR) and enterprise-grade security standards.

• Implement access controls, data encryption, and audit logging for sensitive AI workflows.

   

Research & Innovation

• Stay current with evolving LLM technologies, frameworks and methodologies.

• Evaluate emerging tools and techniques to continuously improve solution quality and delivery speed.

   

Collaboration & Leadership

• Work closely with cross-functional teams to interpret business requirements and recommend LLM-driven solutions.
• Contribute to technical and architectural decisions, ensuring alignment with strategic goals.

• Mentor junior engineers and support knowledge sharing across the team.

   

Operational Excellence

• Take ownership of incident resolution, and bug fixes.
• Create and maintain technical documentation for AI systems, models, and integration workflows.

Requirements:

• English level — B2 or higher
• 5+ years of experience in AI engineering and .NET software development (.NET 10, ASP.NET Core/WebAPI, C#) with frontend experience in React 19 / Next.js 15 / TypeScript, with a focus on Azure-based solutions.
• Experience building intelligent chatbots and autonomous agents (Agentic AI) that perform real actions, plan, reason, and act with minimal human input; familiar with multi-agent orchestration and agent memory.
• Strong expertise in embeddings, search, and prompt engineering.
• Strong understanding of large language models (LLMs), their limitations, and modern capabilities (e.g., GPT, Claude, Gemini).
• Hands-on experience with Azure AI services (Azure AI Foundry, Azure OpenAI, Azure AI Search) and hands-on experience with MCP or similar protocols for secure, scalable integration between AI agents and enterprise systems.
• Familiarity with multi-agent orchestration patterns: coordinator-subagent workflows, maker-checker validation, signal-based confidence scoring, and context preservation across multi-turn interactions.
• Experience designing prompts with explicit evaluation criteria, few-shot techniques, structured output via JSON schemas, and validation/retry loops for reliable LLM responses.
• Awareness of AI-specific security concerns: PII handling, data boundary enforcement, prompt injection prevention, and compliance considerations (e.g., GDPR).

• Ability to write clean, maintainable, and testable code following SOLID principles and Clean Architecture patterns.

   

Desirable Qualities:

• Understanding of AI solution architecture patterns: LLM gateway/service layer, retrieval layer, orchestration/agent layer, and evaluation/observability layer.
• Experience testing AI-enabled components using deterministic seams (mocked LLM clients, retrievers, tools), scenario-based tests, and regression/evaluation datasets for non-deterministic outputs.
• Understanding of retrieval systems (keyword search, vector search, embeddings) and ranking algorithms
• Familiarity with AI/ML versioning practices: prompt version control with semantic tagging, evaluation baseline tracking, dataset versioning, experiment reproducibility.
• Familiarity with deploying, monitoring, and maintaining ML models in production environments.
• Experience with Azure Cosmos DB, Azure Container Apps, Docker containerization
• Familiarity with CI/CD pipelines and DevOps practices.
• AI-102
• Experience with Application Insights telemetry, distributed tracing.
• Prior experience integrating AI agents with enterprise platforms like ConnectWise PSA, Confluence, SPO, etc.

Atlas Technica is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

Avanquest is the software publishing subsidiary of Claranova, a French group of innovative technological solutions, established in many countries, with revenues exceeding $500 million. At Avanquest, we create reliable and intuitive software solutions that satisfy our customers through 3 key product types: PDF software, photo editing software (Inpixio), and utility software to improve PC performance and security. Mainly offered as Software as a Service (SaaS), our products are sold in more than 150 countries and are translated into more than 15 languages worldwide.

Avanquest is:

• a multicultural collaborative group;
• an ambitious company in rapid development;
• a place where each Talent has its place to contribute its know-how and have a real impact on large-scale projects;
• an environment where productive initiatives are always recognized at their true value.

Visit our website : www.avanquestgroup.com

Job details :

We’re building AI deeply into our product stack — from open-source model serving to the interfaces our users interact with every day. As an AI Integration Engineer, you’ll own three interconnected layers: a model-serving REST API, AI-powered web app features, and the orchestration backbone of our document processing platform. This is a high-ownership, full-stack role for an engineer who wants to ship real AI systems, not just call third-party APIs.

(Only for candidates who are currently in Poland)

Model Serving & REST API

Deploy open-source LLMs and expose them through a versioned, production-grade API.

Web App Integration

Embed AI capabilities into our web interfaces — streaming UX, real-time features, and AI-powered components.

Document Processing Orchestration

Build the processing pipelines that extract, classify, transform, and act on documents at scale.

Key Responsibilities :

Model Serving & API

• Evaluate and deploy open-source LLMs via vLLM, Ollama, or TGI on GPU infrastructure;
• Design and maintain a REST API with auth, rate limiting, versioning, and API documentation;
• Optimize inference: quantization, batching, and concurrency tuning for latency/cost trade-offs;
• Own observability — latency, throughput, GPU utilization, and cost-per-call dashboards.

Web App Integration

• Integrate AI features into our web apps and document processing platforms;
• Build AI-powered UI components: chat interfaces, inline suggestions, smart search, and summarization widgets;
• Collaborate with frontend engineers on UX patterns specific to async and streaming AI responses;
• Manage API keys, usage metering, and per-user AI feature flags on the client layer.

Document Orchestration

• Design multi-step orchestration pipelines for document ingestion, parsing, classification, and extraction;
• Integrate OCR, layout detection, and chunking strategies for PDFs, Word docs, spreadsheets, and scanned images;
• Build RAG pipelines with hybrid retrieval (dense + sparse) over document corpora;
• Design and optimize prompts for extraction, summarization, classification, and conversational AI workflows;
• Implement task queues and async workers to handle high-volume document processing reliably.

Platform & Collaboration

• Define evals and quality benchmarks for extraction accuracy, classification, and generation quality;
• Write technical docs, so the platform is maintainable by the whole team and accessible for technology partners;
• Partner with product, data science, and DevOps/MLOps to ship AI features reliably and at scale;
• Stay current on the open-source model ecosystem and advise on model selection and upgrades.

Requirements:

• 3+ years of software engineering experience spanning backend, API design, and web technologies;
• Proven experience deploying or integrating LLMs in production — open-source model experience strongly preferred;
• Hands-on knowledge of REST API design principles and at least one API framework;
• Experience building or integrating AI features in a web app, including streaming/async response handling;
• Familiarity with document processing workflows — parsing, OCR, extraction, or classification at scale;
• Comfort designing multi-step orchestration pipelines with task queues, retries, and failure handling;
• Strong communication skills; able to reason about trade-offs across latency, cost, and accuracy with stakeholders.

What we offer:

• A fair compensation for your value;
• Bonus program, paid vacation leave and competitive corporate benefits;
• We are committed to maximizing your potential and ensuring your professional development.
• Interaction with local and international teams;
• A friendly and collaborative work environment, where authenticity and well-being are a priority.

CodeTiburon is looking for a Senior Backend Engineer (LLM & AI Infrastructure) to join our team and work closely with one of our long-term clients.

About the project:

We are a deep-tech company building state-of-the-art LLM-based assistants augmented with knowledge graphs. The platform focuses on combining large language models with structured knowledge systems to create more accurate, reliable, and scalable AI assistants.
 

The project involves building robust backend infrastructure that powers AI applications, enabling scalable model inference, retrieval systems, and knowledge graph integrations within a multi-tenant SaaS environment.
 

Responsibilities:
 

• Architect, deploy, and operate scalable backend services and multi-tenant SaaS systems in Kubernetes-based cloud environments
• Design and operate high-throughput data pipelines (batch and streaming)
• Build APIs and services integrating LLM inference, retrieval systems, and knowledge graphs
• Productionize research-grade AI systems into reliable distributed systems
   

Requirements (Must Have):
 

• 7+ years of backend engineering experience
• Strong experience with data pipelines and SaaS architectures
• Strong proficiency in Python
• Deep understanding of distributed systems and cloud-native environments
• Strong hands-on experience with Kubernetes in production environments. Candidates should be comfortable deploying, operating, scaling, and troubleshooting distributed applications on Kubernetes and have practical experience with its core ecosystem (networking, storage, observability, autoscaling, and CI/CD integration).
   

Nice to Have:
 

• Go and/or Scala experience
• Hands-on experience with LLM systems (RAG, model serving, evaluation)
• Experience with knowledge graphs, vector databases, or hybrid retrieval architectures
  
   

Ideal for engineers who enjoy building production-grade systems around cutting-edge AI research.

 

If this sounds like you and your experience aligns with most of the qualifications above, we’d love to hear from you. Please send us your CV.

Thank you for your interest. Each application is carefully reviewed, and candidates whose experience and background best align with the role will be contacted regarding the next steps.

Full Remote | Full-Time | b2b | Flexible Schedule|Product Company

Overview

At eMenu, we are revolutionizing the hospitality industry through innovative iPad menus served to guests in restaurants instead of traditional paper menus.

Our mission is to transform how people interact with menus and dining experiences, setting new standards for customer engagement and operational excellence.

As we continue to grow, we are building intelligent, responsive systems – including a conversational AI Waiter and upgrading our internal tools. You will contribute to redefining the smart restaurant experience by building robust backend systems, integrating language models, and developing automated features that empower both our users and internal teams.

Role Summary

We are looking for a hands-on Python Backend Engineer to take ownership of internal automation systems, tackle complex backend integration challenges, and contribute to the backend systems supporting our conversational “AI Waiter.”

This role emphasizes strong software engineering fundamentals, project ownership, and problem-solving. While your primary focus will be backend development, you will heavily utilize AI-driven tools, automation systems, and third-party integrations to get the job done.

Key Responsibilities

• Backend Engineering & Automations: Build and maintain internal APIs using Python, FastAPI, and SQL databases. Develop backend automations, alerting systems, and complex third-party API integrations.
• AI Waiter Contributions: Contribute to the backend systems and integrations that power our conversational AI Waiter.
• AI Tools & Data Processing: Develop and improve internal tools by integrating LLMs for data processing, alongside other third-party AI services (e.g., text and image processing APIs).
• Testing & Code Quality: Write maintainable, robust code and implement automated tests to ensure system reliability.
• Web Architecture: Design clear, scalable APIs to ensure seamless and reliable client-server data flow.
• Cloud Infrastructure: Work within an AWS cloud environment and support application deployments when needed.

Requirements

Must-Have:

• 3–5 years of experience in software development with a strong focus on backend engineering.
• Strong proficiency in Python and modern frameworks (e.g., FastAPI).
• Solid experience working with SQL databases.
• Deep understanding of web architecture, REST APIs, and client-server interaction.
• AI Integration: Practical, hands-on experience integrating and working with Large Language Models (LLMs) via APIs.
• Cloud Experience: Familiarity with AWS or similar cloud platforms to understand the environment and support deployments.
• Fluency in English (Upper-Intermediate or higher) and one additional language (Ukrainian, Russian, or Hebrew).

Working Style & Expectations:

• Project Ownership: Ability to work independently with minimal supervision, define solutions end-to-end, and take responsibility for systems in production.
• Bias for Action: Strong prioritization skills with a focus on delivering production-ready solutions quickly.

Nice to Have:

• Reverse Engineering: Ability to analyze and work with undocumented APIs or systems.
• Familiarity with recommendation systems or predictive modeling.
• Experience with frontend frameworks (e.g., Vue.js).

Work Environment and Schedule

• Salary: Up to $4,500 per month.
• Contract: B2B contract – unlimited.
• Time Off: 10 calendar days of PTO per year; up to 5 sick days per year.
• Stability: Long-term role with a focus on retention.
• Flexible Schedule: 40 hours per week, fully flexible distribution (aligned mostly with European hours, occasional US overlap).
• Minimal Meetings: No daily standups, weekly updates only.

Interview Process

1. Step 1 – Introductory Call
2. Step 1.5 – Take-Home Test (before the technical interview)
    
3. Step 2 – Technical Interview with Ilya (~1 hour total)
4. Step 3 – Cultural Fit Interview

We are an international developer of IT products for medicine and related services. Our products are used by more than 5,000 doctors and more than 1,000 clinics in Europe.

The goal is to attract the largest possible audience to the main projects - clinics and doctors up to 3 million. As well as expanding unique traffic to 10 million per day until 2025. We are looking for AI Engineer / AI Architect with deep knowledge of Fundamental Mathematics, Probability Theory and Bayesian Statistics, Graph Algorithms, Mathematics of Transformers, Hybrid AI Approaches, Uncertainty Assessment.

Must-have skills are:
1. ML and Data Science (Core):
- Expert in NLP & LLM: Deep understanding of Transformers architecture (BERT, RoBERTa, GPT, T5, Llama/Mistral). Experience with Large Language Models (LLM) – not prompt engineering, but fine-tuning (PEFT, LoRA) of open-source models on specific datasets.
- RAG (Retrieval-Augmented Generation): Experience building RAG systems is required. The AI ​​must be able to search for information in different data sources.
- Named Entity Recognition (NER) & Information Extraction: Ability to extract specific entities from unstructured text.
- Tabular Data & Classic ML: Confident use of classical algorithms (XGBoost, CatBoost, LightGBM) for analyzing structured data and building probability scoring models.
- Frameworks: PyTorch (preferred) or TensorFlow/Keras. Proficiency in HuggingFace, Scikit-learn, Pandas, and NumPy.

2. MLOps:
- Experience wrapping models in APIs (FastAPI, Flask) with minimal latency. 

- Docker knowledge and experience working with local GPU instances are required.
- Experience working with vector databases (Pinecone, Milvus, Qdrant, ChromaDB) for quickly searching for similar cases or working with knowledge bases.

- ML Pipelines: Experience building model training and retraining pipelines (Airflow, MLflow, DVC).

3. MedTech and Security:
- Practical working experience with sensitive personal data and GDPR/DSGVO requirements.

- It is important to be able to deploy and optimize local models (Llama 3, Mistral, Mixtral) within a closed company environment to ensure data privacy.
- Skills in quantization and distillation of models to speed up work without significant loss of quality.

Nice-to-have skills:
- Experience working specifically with medical data (EHR, EMR; HL7, FHIR, ICD-10/11 standards). 

- Knowledge of Interpretability/Explainable AI (XAI) methods. The end user should understand why the AI ​​suggested a particular option (SHAP, LIME).

- Experience with multimodal models.

What we offer:
- Paid holiday for 28 working days.
- Transparent incentive system.

Working with us means:
- Unique technological and innovative projects.
- Gaining experience in modern western world practices in security, scalable architecture, etc.
- In-depth knowledge of telemedicine.
- Working with Big data, high load, distributed computing and data buses.
- Socially important projects.
- Opportunity to influence both technology and product solutions.
- Profitability and stability of a large European company.

We seek a Strong Middle ML / AI Engineer to join our team on a humanitarian transparency platform. This is a hands-on engineering role focused on building machine learning models and data pipelines — primarily around deduplication, anomaly detection, and statistical analysis of humanitarian data.

 

🧙‍♂️ In This Role You Will

• Design, build, and maintain ML models for deduplication, anomaly detection, and predictive analytics.
• Work with structured humanitarian data — beneficiary records, financial transactions, procurement flows.
• Build and maintain data processing pipelines using Python (Pandas, NumPy, scikit-learn).
• Collaborate with the backend team to expose ML/AI outputs via APIs.
• Monitor model performance and iterate based on real-world feedback.
• Document your work and support knowledge sharing within the team.
  
  
   

🎯 You May Be Interested If

• You enjoy working with messy, real-world data and turning it into reliable ML outputs.
• You care about model quality, reproducibility, and practical impact.
• You're comfortable applying classical ML approaches to solve concrete business problems.
• You're excited to collaborate in a remote, cross-functional environment.
• You value autonomy, clear communication, and working in a supportive, small-team setting.
  
   

👌 Role Requirements

• 3+ years of experience in ML engineering, data science, or a closely related role.
• Strong Python skills: Pandas, NumPy, scikit-learn.
• Experience building and deploying ML models in production environments.
• Solid understanding of data pipeline design and data quality practices.
• Familiarity with cloud platforms (Azure or AWS) for ML workloads.
• Proficiency with Git and standard development tooling.
• English proficiency at B1 level or higher.
   

Nice to have:

• Experience with PyTorch or TensorFlow.
• Hands-on experience with LLM integrations (OpenAI, Anthropic, or similar).
• Familiarity with ML experiment tracking tools (e.g. MLflow, Weights & Biases).
• Experience with GIS or geospatial data.
• Background in fraud detection, entity resolution, or deduplication tasks.
• Familiarity with Docker and CI/CD workflows for ML pipelines.
  
   

💻 Working Conditions

• Employment Type: Full-Time
• This is a fully remote position with the entire team across Ukraine.
• 20 working days of paid vacation per year.
• The team collaboratively defines deadlines, ensuring a balanced and manageable workload. The focus is on delivering commitments on time.
• Flexible working hours to promote a healthy work-life balance.

CodeTiburon is looking for an AI Research Engineer — Neuro-Symbolic Reasoning & Generative AI to join our team and work closely with one of our long-term clients.
 

About the Project

Our client is an applied AI lab specializing in formal knowledge representation and next-generation reasoning. The project is dedicated to building “semantic AI” capable of deep, grounded reasoning over complex structures such as texts, arguments, and intellectual traditions.
 

The core mission is to combine the power of modern LLMs with the rigor of neuro-symbolic methods and Knowledge Graphs. By focusing on domains with the highest logical standards, the team aims to set new benchmarks for responsible and intelligent AI behavior across the industry.
 

About the Role:

You will be employed by CodeTiburon and embedded into the client’s research team on a long-term basis. Working alongside a Senior Applied Scientist, you will help transform research ideas into production-quality systems and reproducible experiments.

While the Applied Scientist defines research directions and designs novel algorithms, you will be responsible for implementing experiments, fine-tuning models, building evaluation frameworks, developing data and reasoning pipelines, and iterating toward robust, production-ready solutions.

You will serve as a key engineering partner in the research process—proposing implementation strategies, identifying engineering constraints early, and translating hypotheses into measurable results.

 

Topics You Will Work On:

• Experiment Implementation. Translate research designs from the Applied Scientist into clean, reproducible Python experiments—covering neuro-symbolic architectures (LTN, DeepProbLog, LARK), hybrid LLM+KG pipelines, and RAG systems.
• Knowledge Graph & Ontology Engineering. Build and maintain tooling around RDF/OWL 2 ontologies, SPARQL/Cypher queries, and graph databases (Neo4j, Amazon Neptune) to feed downstream reasoning modules.
• RAG/KG Pipeline Development/Integration. Implement and iterate on retrieval-augmented generation/KG pipelines—combining dense, sparse, and structured retrieval over heterogeneous corpora with provenance tracking.
• LLM Fine-Tuning & Evaluation. Run supervised and RLHF/DPO fine-tuning experiments; build evaluation harnesses that include symbolic verification layers, benchmarks, and regression suites.
• Research Infrastructure. Own the experiment tracking, dataset pipelines, and MLOps scaffolding (MLflow, SageMaker, Vertex AI) that let the team iterate quickly and reproduce results reliably.
• Literature & Prototyping. Stay current with relevant publications; rapidly prototype ideas from newly published methods and assess their fit against the client’s goals.
  
   

Required Qualifications
 

Education:

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, AI Planning, Machine Learning, Cognitive Science, Formal methods, or a closely related field.
 

Language Skills:

Professional working proficiency in English, both written and spoken, with the ability to participate in technical discussions, read and implement research papers, document findings, and collaborate effectively in an international research environment (Strong B2/C1).

Core Technical Skills:

• Generative AI / LLMs: solid working knowledge: transformer architecture, prompt engineering, RAG, tool use, fine-tuning (SFT/LoRA);
• Machine Learning: proficient: supervised/unsupervised learning, representation learning, experiment design;
• Neuro-Symbolic Concepts: familiar: exposure to at least one framework (s.t. LTN, DeepProbLog, NSCL, LARK) or symbolic AI coursework; eager to deepen;
• Knowledge Graphs: working knowledge: graph data models, SPARQL or Cypher queries, at least one graph DB (Neo4j or equivalent);
• Software Engineering: strong production Python; unit testing, version control, CI basics
   

Experience:

• Proven ability to independently implement research ideas, build reproducible experiments, and contribute to production-ready AI systems.
• Demonstrated ability to implement models and algorithms from research papers with minimal hand-holding.
• 3–5 years of hands-on experience in ML/AI engineering, research engineering, or a closely related role (internships and research assistant positions count).
• Experience with experiment tracking tools (MLflow, Weights & Biases, or similar) and reproducible research practices.
• Experience working with cloud-native ML infrastructure on AWS, GCP, or Azure.
   

Preferred Qualifications:

• Exposure to formal knowledge representation: OWL 2, RDF/RDFS, description logics, or first-order logic formalisms.
• Familiarity with agentic AI frameworks (tool-use chains, multi-step planning, multi-agent coordination).
• Experience with LLM evaluation and red-teaming for factual accuracy, logical consistency, or safety.
• Contributions to open-source ML/NLP/KR projects or co-authorship on an arXiv pre-print or workshop paper.
• Exposure to formal verification, SAT/SMT solvers, or answer set programming.
• Experience with low-resource or domain-specific fine-tuning of foundation models.
• Familiarity with graph neural networks or heterogeneous graph transformers.
• Experience with AI coding tools such as Claude Code or Cursor.
• Proven ability to deliver production-ready artifacts.
   

Responsibilities:

• Implement, run, and iterate on experiments designed by the Applied Scientist; maintain clean, well-documented, reproducible code.
• Build and maintain RAG pipelines, knowledge graph integrations, and neuro-symbolic prototype systems.
• Design and execute evaluation harnesses—combining automated metrics, symbolic checks, and human evaluation—to measure model quality and catch regressions.
• Keep experiment infrastructure (data pipelines, tracking, compute provisioning) reliable and easy for the whole team to use.
• Read relevant papers and proactively surface implementation options or potential issues before they become blockers.
• Collaborate daily with the Applied Scientist, ML engineers, and content teams to align research outputs with product requirements.
• Contribute to internal documentation, design reviews, and code reviews; grow your skills through mentorship from senior researchers.
• Uphold scientific rigour, reproducibility, and responsible AI practices in every piece of work you ship.
• Working closely with the production owners.

If this sounds like you and your experience aligns with most of the qualifications above, we’d love to hear from you. Please send us your CV.

Thank you for your interest. Each application is carefully reviewed, and candidates whose experience and background best align with the role will be contacted regarding the next steps.

Quantum is a global technology partner delivering high-end software products that address real-world problems. 

We advance emerging technologies for outside-the-box solutions. We focus on Machine Learning, Computer Vision, Deep Learning, GIS, MLOps, Blockchain, and more.

Here at Quantum, we are dedicated to creating state-of-the-art solutions that effectively address the pressing issues faced by businesses and the world. To date, our team of exceptional people has already helped many organisations globally attain technological leadership.

We constantly discover new ways to solve never-ending business challenges by adopting new technologies, even when there isn't yet a best practice. If you share our passion for problem-solving and making an impact, join us and enjoy getting to know our wealth of experience!

About the position

Quantum is expanding the team and has brilliant opportunities for a Senior DS / AI Engineer. The client is a technological research company that utilises proprietary AI-based analysis and language models to provide comprehensive insights into global stocks in all languages. Our mission is to bridge the knowledge gap in the investment world and empower investors of all types to become "super-investors." Through our generative AI technology implemented into brokerage platforms and other financial institutions' infrastructures, we offer instant fundamental analyses of global stocks alongside bespoke investment strategies, enabling informed investment decisions for millions of investors worldwide.

Must have skills:

• 5+ years of commercial experience as a Data Science / AI Engineer
• Strong Python skills and software engineering fundamentals
• Strong understanding and experience in the development of high-load production systems
• 2+ years of experience with LLM and NLP-related tasks
• Hands-on experience with RAG and vector DBs
• Knowledge of modern DL architectures
• Experience with at least one of the Deep Learning frameworks (Tensorflow, PyTorch)
• Experience with SQL and NoSQL databases
• Good knowledge of Kubernetes
• Strong experience in development with AI tools (Claude Code, Cursor)
• At least an Upper-Intermediate level of English (spoken and written)

Nice to have skills:

• Familiarity with MCP (Model Context Protocol)
• Experience with production LLM frameworks (vLLM)
• Experience in training LLMs
• Experience with Cloud Computing Platforms (AWS, GCloud, Azure)
• Participation in Kaggle competitions

Your tasks will include:

• Full-cycle data science projects
• Data analysis and data preparation
• Development of NLP solution and AI-based chatbots
• Developing models and deploying them to production
• Sometimes, this will require the ability to implement methods from scientific papers and apply them to new domains

We offer:

• Delivering high-end software projects that address real-world problems
• Surrounding experts who are ready to move forward professionally
• Professional growth plan and team leader support
• Taking ownership of R&D and socially significant projects
• Participation in worldwide tech conferences and competitions
• Taking part in regular educational activities
• Being a part of a multicultural company with a fun and lighthearted atmosphere
• Working from anywhere with flexible working hours
• Paid vacation and sick leave days

Join Quantum and take a step toward your data-driven future.

About the Role

We are looking for a strong engineer who can help us build AI-powered tools and automation for real business problems.

We work with large amounts of business data and are building internal systems for verification, enrichment, and automation. We need someone who can quickly understand a problem, suggest a practical solution, and build it.

This role is very hands-on. We are looking for a builder — someone who likes solving problems, moving fast, and turning ideas into working products.

What You’ll Do

• Build AI agents and automation workflows
• Work with LLMs (OpenAI, Claude, etc.)
• Build tools that help automate manual work
• Integrate APIs and external systems
• Improve speed, quality, and reliability of workflows
• Quickly test ideas and move them into production
• Work closely with business and product teams

What We’re Looking For

Must Have

• Strong Python skills
• Experience building real products, systems, or automation
• Experience working with LLMs and AI tools
• Experience with APIs and integrations
• Ability to solve problems independently
• Fast execution and ownership mindset

Strong Plus

• Deep experience with AI-native development workflows
• Daily use of Claude Code, Codex, Cursor, or similar agentic coding tools
• Experience building products using AI-assisted engineering environments

Nice to Have

• Experience with LangGraph, CrewAI, AutoGen, or similar frameworks
• Experience with RAG, vector databases, or AI memory systems
• GitHub or personal projects
• Experience working with large amounts of data

Important

We are not looking for someone who only builds no-code automations.

We are looking for someone who can understand a business problem, think through the solution, and build working systems.

Please share examples of projects you built and your GitHub (if available).

We Offer

• Remote work
• Long-term collaboration
• High-impact projects
• Modern AI tools and technologies
• Fast-moving environment

OneAcademy is looking for a Video Designer with strong motion design skills and hands-on experience using AI tools for commercial video production. We are building a fast-scaling education brand focused on high-performance digital content, and we need someone who can combine creativity with speed and ownership.

What You’ll Do

• Create short-form video content (Reels, YouTube Shorts, TikTok) using AI-assisted production tools
• Develop static creatives for social media and marketing campaigns
• Optimize video production workflows to improve speed, efficiency, and scalability
• Adapt content to different platforms and formats
• Work within tight deadlines while maintaining quality standards

Hard Skills & Experience

• 2+ years of strong, hands-on experience with Adobe After Effects
• Basic proficiency in Adobe Photoshop
• At least 6 months of practical experience using AI tools for commercial video production (e.g., Kling, VEO, Sora, or similar)
• Experience with prompt engineering and cost optimization of AI tools (tokens / credits management)
• Solid understanding of composition, color correction, and storytelling fundamentals

Soft Skills We Value

• High level of independence and self-management
• Strong sense of responsibility and ownership over tasks and results
• Proactive mindset
• Fast learner, comfortable adapting to new tools and workflows
• Ability to operate in a performance-driven environment

What We’re Looking For

This role is ideal for someone who is not just an executor, but a creator who understands both aesthetics and performance metrics. You should be comfortable experimenting with AI tools, iterating quickly, and continuously improving production efficiency.

If you thrive in a fast-moving, digital-first environment and want to shape the visual language of a growing education brand — we’d love to hear from you.

Why OneAcademy

• Direct ownership over growth of a core educational product
• Opportunity to build a scalable, data-driven education business
• Global product with long-term LTV focus
• High level of autonomy and fast execution
  
  What We Offer
• Ownership of acquisition strategy 
• Work with large budgets and high-scale performance campaigns
• Direct influence on unit economics and growth metrics
• Opportunity to build and shape your own team
• Remote work with flexible schedule
• Work in a fast-growing global prop trading firm
• Performance bonuses and incentives

About the Project

The client is looking for a Software Engineer who operates at the intersection of high-scale frontend engineering and cutting-edge Applied AI.

In this role, you won’t just "build UI", you will design and deliver intelligent, cloud-native user experiences for our Maritime Intelligence Agentic platform. Our systems are used by defense agencies, governments, and global enterprises to secure trade and enforce compliance at sea. You will be expected to work autonomously, influence frontend architecture and UX standards, and significantly raise the bar for AI-assisted development within the team.

Required skills:

• A Professional Engineer: You have 7+ years of professional frontend or full-stack engineering experience, including 2+ years of experience building AI-powered products, agentic interfaces, or intelligent workflows in production at scale.
• An AI-Native Engineer: You are excited about agentic development and AI-enhanced user experiences. You are proficient in AI-based development workflows utilizing tools like Claude Code, Cursor, or Copilot in a highly autonomous fashion, and are a strong, independent individual contributor who can work effectively alone or in small, ad-hoc groups.
• Frontend & Cloud Architecture: You have proven experience building modern frontend applications with React, Next.js, TypeScript, GraphQL/REST APIs, and distributed cloud-based architectures. Experience with real-time systems, event-driven architectures, and cloud-native environments is highly valued.

• A Craftsman: You take pride in clean code, thoughtful UX/UI design, frontend performance, accessibility, and production-grade quality. You are accountable, independent, and thrive in ambiguous environments.

   

Technical Requirements

• Frontend Stack (Mandatory): Proven expertise with React.js, Next.js, TypeScript, modern frontend architecture patterns, state management, and component-driven development.
• AI & Agentic Interfaces: Experience integrating AI capabilities into frontend applications, such as conversational UI, AI copilots, streaming responses, or agentic workflows. Experience with frameworks such as LangChain, LangGraph, OpenAI SDKs, Vercel AI SDK, or similar is a strong plus.
• Cloud & Infrastructure: Solid understanding of modern frontend deployment environments, CI/CD pipelines, cloud-native applications, and observability practices. Familiarity with AWS, Kubernetes, Terraform, or serverless environments is a plus.

• Nice to Have: Experience with backend technologies such as Python, Node.js, GraphQL services, or distributed systems.

   

Scope of work:

• Architect & Build: Design and deliver scalable, responsive, and high-performance frontend applications, primarily using React/Next.js, within a distributed system architecture.
• AI Integration: Lead the integration of Generative AI capabilities into frontend experiences, including AI-powered workflows, copilots, conversational interfaces, and real-time intelligence visualization tools.
• Frontend Operations & Observability: Implement monitoring, analytics, performance optimization, and observability practices to ensure reliability, usability, and smooth user experience in production.
• Elevate Engineering: Drive frontend engineering quality and speed by implementing AI-SDLC practices, enforcing high standards through code reviews, component testing, E2E automation, and continuous UX/performance evaluation.
• Mentor & Innovate: Evaluate emerging AI and frontend tooling, mentor fellow engineers, and contribute directly to the evolution of our frontend architecture and technical roadmap.

The role

As a Senior AI Engineer for LLM Applications, you will own the LLM and information-retrieval layer of the Digital IP platform. You will turn raw patent documents — claims, descriptions, drawings, file wrappers, and prior art — into structured, queryable, decision-grade signal for inventors, the IP Council, product managers, and executives.

This is a high-leverage individual-contributor role on a small, senior team. You will partner closely with the Architecture Lead, with the IP Council, and with R&D leaders across Ingersoll Rand's business units. You will ship production systems — not prototypes — and your work will be visible at the executive level.
 

What you will do

• Design, build, and operate the LLM-driven services that power patent ingestion, classification (IPC/CPC), claim and entity extraction, semantic search, and prior-art retrieval across the Digital IP platform.
• Own the lifecycle of LLM applications on patent data: retrieval design, prompt and chain engineering, structured-output design, evaluation, and continuous improvement on top of managed LLMs.
• Build retrieval systems over Ingersoll Rand's internal corpus and external patent databases (USPTO, EPO, WIPO), including embedding strategy, vector indexes, and hybrid lexical/semantic search.
• Develop rigorous offline and online evaluation: gold sets co-curated with the IP Council, regression suites, hallucination and citation-faithfulness checks, and human-in-the-loop review workflows.
• Productionise LLM-driven systems end-to-end — retrieval indexes, prompt and chain wiring, serving, monitoring, latency and cost control — on Snowflake (Cortex, Snowpark).
• Translate ambiguous business questions from inventors, patent attorneys, and executives into well-scoped problems and shippable products.
• Contribute to the architectural direction of the Digital IP platform: data contracts, service interfaces, and reusable patterns that scale across business units.
• Raise the team's bar on evaluation rigour, documentation, and reproducibility, and mentor engineers and analysts who interact with the LLM stack.
   

What we are looking for

Required

• 5–8 years of professional software engineering experience, with the last 2+ years focused on shipping LLM-driven applications.
• At least one substantial LLM application that you took to production — RAG, agents, or LLM-driven workflows — and can speak to how you designed retrieval, prompts, evaluation, and post-launch monitoring.
• Strong working knowledge of information retrieval: embeddings, vector search, hybrid lexical+semantic approaches, and chunking strategy.
• Hands-on experience with Snowflake — Cortex, Snowpark, or building data and LLM workloads directly on the warehouse.
• Solid software engineering practice: clean Python, tests, version control, code review, and CI/CD.
• A disciplined approach to evaluation: gold sets, regression suites, hallucination and citation-faithfulness checks, and an instinct for measuring quality before shipping.
• Track record of working directly with non-engineering stakeholders — domain experts, analysts, or executives — and translating their needs into shipped systems.
• Excellent written and spoken English; ability to overlap several hours per day with Central European Time.

Strongly preferred

• Experience with structured information extraction at scale (entities, relations, attributes) from messy, multi-format corpora.
• Experience designing human-in-the-loop and annotation workflows with subject-matter experts.
• Familiarity with LLM application tooling — orchestration frameworks, observability, prompt/version management, cost and latency monitoring in production.

Nice to have

• Direct experience with patent or other long-form legal/technical documents: claim structure, IPC/CPC taxonomies, USPTO/EPO/WIPO data, or PATSTAT.
• Fine-tuning of LLMs or embedding models for domain-specific tasks, where the case for it was clear.
• Classical NLP or machine-learning background (training models, MLOps lifecycle).
• Background in mechanical, electrical, chemical, or industrial engineering — or any other domain that makes patent content feel familiar rather than foreign.
• Experience working in a regulated or IP-sensitive environment.