Machine Learning Engineer

Location: Europe only
Employment type: Full-time, Remote
 

About the Role

We're looking for a Middle/Senior Generative AI / Machine Learning Engineer to help build production-grade AI systems from classical ML and deep learning models to modern LLM-powered applications. 
You'll work across the full lifecycle: designing and training models, building RAG and agentic pipelines, and deploying everything into scalable, well-architected production systems.

This role blends hands-on ML engineering with GenAI/LLM development, MLOps, and data engineering, ideal for someone who enjoys working across the stack rather than staying in one narrow lane.
 

What You'll Do

• Design, train, and deploy classical ML and deep learning models (PyTorch, TensorFlow/Keras, scikit-learn) for tasks such as time series analysis, anomaly detection, forecasting, and optimization.
• Build production LLM applications: RAG pipelines, embeddings, semantic search, document processing, and chunking strategies.
• Develop AI agents and multi-step workflows using LangChain, LangGraph, and MCP (Model Context Protocol).
• Apply prompt engineering best practices to improve reliability and output quality of LLM-based systems.
• Deploy and serve models using Docker, Kubernetes, and FastAPI/Flask.
• Build and maintain data pipelines (ETL/ELT) using Apache Airflow, Spark/Databricks, and BigQuery, with a focus on data quality.
• Contribute to system architecture decisions — applying principles like Domain-Driven Design, event-driven architecture, and microservices to build highly available, distributed systems.
• Work with cloud infrastructure, primarily Azure (AWS experience is a plus); experience with Azure OpenAI is a strong advantage.
   

What We're Looking For

Core:

• Strong Python skills with solid experience in classical ML and deep learning (PyTorch, TensorFlow/Keras, scikit-learn).

GenAI / LLM:

• Hands-on experience building production LLM applications: RAG, embeddings, chunking, semantic search, document processing.
• Experience with LangChain, LangGraph, AI agents, and MCP (Model Context Protocol).
• Solid understanding of prompt engineering.

Machine Learning / Data Science:

• Experience with deep learning and classical ML techniques applied to time series, anomaly detection, forecasting, and optimization problems.

MLOps / Deployment:

• Experience with Docker, Kubernetes, and model serving via FastAPI or Flask.

Data Engineering:

• Experience with ETL/ELT processes, Apache Airflow, Spark/Databricks, BigQuery, and building reliable data pipelines with strong data quality practices.

Cloud:

• Experience with Azure (preferred); AWS experience is a plus. Experience with Azure OpenAI is an advantage.

Architecture / System Design:

• Familiarity with architecture frameworks and practices such as TOGAF, C4, ArchiMate, Domain-Driven Design, event-driven architecture, high availability, distributed systems, and microservices.

We offer:

• Remote Work Environment. Work from anywhere and be part of a geographically diverse team;
• Stay Ahead of the Curve. Work with cutting-edge technologies;
• Possibility to influence on processes;
• Paid 16 vacations and 14 sick leaves per year;
• Flexible working hours;
• Possibility of payment by FOP, or Payoneer/Wise;
• Compensation program for purchasing new laptops;
• Paid professional certifications & educational courses;
• Paid English classes;
• Friendly atmosphere with quarterly team buildings;
• No bureaucracy.
   

We respect your time, so inform in advance that the hiring process includes a 2-step interview:

• General interview
• Technical interview

Please note that a background check (reference check) is a mandatory stage of our interview process for this role.
 

Ready to try your hand? Do not pull the cat’s tail and send your CV without a doubt!

For those candidates who don’t get any feedback after applying: We are so thankful for your time and attention to our vacancy! However, sometimes we get too many CVs at once, and it would take several days to answer them all. That’s why we reach out only to relevant candidates. We hope for your understanding.