Design and implement scalable ML infrastructure to support model development and deployment
Develop and maintain evaluation frameworks for Large Language Models (LLMs), including RAG-based systems
Evaluate model performance using tools such as RAGAS, DeepEval, or similar frameworks
Contribute to the development of GenAI and Agentic AI solutions
Build and enhance monitoring systems for ML models in production environments
Collaborate with cross-functional teams including data scientists, engineers, and product stakeholders
Apply best practices in model versioning, testing, and deployment pipelines
Optimize performance, scalability, and cost efficiency in cloud environments

5+ years of strong experience with Python for machine learning and backend development
Hands-on experience with ML infrastructure and model deployment pipelines
Experience evaluating LLMs using frameworks such as RAGAS, DeepEval, or similar
Solid understanding of GenAI concepts, including LLMs and Agentic AI systems
Experience working with Retrieval-Augmented Generation (RAG) architectures
Proficiency in cloud platforms such as AWS, GCP, or Azure
Familiarity with monitoring and observability tools for ML systems
Ability to work independently in a remote, distributed environment
Advanced English level

Machine Learning Engineer

Key skills