Senior / Lead Machine Learning Engineer - LLM & GenAI Applications (relocation to Cyprus)

EPAM Systems is recruiting for a Senior / Lead Machine Learning Engineer to join their team remotely in Nicosia, Cyprus. The role involves designing and building enterprise-scale applications utilizing Large Language Models and Generative AI for AI-driven solutions on enterprise platforms.

• 6+ years of experience in software or ML engineering, with hands-on expertise in LLM-based applications
• Strong proficiency in Python and knowledge of software engineering fundamentals (design patterns, TDD)
• Practical experience with LangChain and/or LangGraph, RAG architectures, and vector databases
• Experience building and deploying NLP/ML models using frameworks such as Transformers and spaCy
• Hands-on MLOps experience with Docker, Kubernetes, CI/CD, and AWS (SageMaker experience preferred)
• Knowledge of REST API development and event-driven microservice architectures
• Familiarity with prompt engineering, AI agent frameworks, and GenAI evaluation methodologies
• Exposure to mentoring or leadership roles in engineering teams
• Strong communication skills and fluency in English (written and spoken)

• Design and deploy production-grade Retrieval-Augmented Generation (RAG) systems using LangChain or LangGraph and vector databases
• Build and orchestrate LLM-based conversational AI and chatbots for enterprise-scale environments
• Architect AI agent workflows applying best practices in prompt engineering and evaluation
• Develop and deploy ML/NLP models using Python, Transformers, and modern frameworks
• Implement and manage MLOps practices including CI/CD, containerization (Docker, Kubernetes), and cloud deployment (AWS SageMaker or equivalent)
• Develop microservices and RESTful APIs using FastAPI or similar frameworks following TDD principles
• Lead and mentor ML/Python engineers ensuring technical excellence and delivery efficiency
• Collaborate with cross-functional teams to convert business requirements into scalable AI solutions
• Apply monitoring and observability tools for GenAI evaluation and production-quality metrics
• Drive technical innovation, ensuring system reliability and performance at scale

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