Senior AI Engineer / Data Scientist

Overview

In this role you will design, build and deploy Generative AI and Agentic AI solutions for enterprise problems, working at the core of EPAM’s Data & AI Practice. You’ll create multi-agent systems, RAG pipelines, and production-grade AI applications that scale across industries. You will shape architecture, raise the bar for engineering practices, and collaborate with cross-functional teams to deliver transformative AI capabilities.

Pay / Benefits

• hybrid working mode
• high-growth environment

Responsibilities

• Design, build, and deploy Generative AI and Agentic AI solutions from prototyping through production
• Develop and optimize multi-agent systems using LangGraph, CrewAI, AutoGen, and Semantic Kernel
• Implement orchestration patterns including planner/executor, supervisor/worker, and tool-calling workflows
• Design and build RAG pipelines with embeddings, chunking, hybrid search, and retrieval evaluation
• Develop orchestration engines supporting multi-step planning, delegation, and fallback paths for agent workflows
• Implement integration and communication patterns via MCP, A2A, OpenAPI, REST, and gRPC
• Build production-grade Python APIs and microservices integrating with enterprise systems and AI services
• Apply observability and monitoring solutions (Langfuse, Arize, Grafana) to ensure system reliability
• Contribute to solution architecture, best engineering practices, and documentation

Key requirements

• Bachelor’s/Master’s in Computer Science, Data Science, or related field with 4+ years’ experience, or Ph.D. with relevant experience
• Strong engineering experience with Python, APIs, microservices, debugging, and code review
• Proven experience building and deploying Generative AI or Agentic AI applications in production
• Deep understanding of LLM concepts, RAG patterns, prompt design, and evaluation methodologies
• Experience with multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel)
• Familiarity with orchestration strategies like planner/executor and tool calling
• Knowledge of MCP, A2A protocols, and OpenAPI-based integration methods
• Strong experience with cloud environments, ideally Azure (Azure OpenAI, AI Foundry, AI Search)
• Competence in containerized deployments, CI/CD, and MLOps tooling (MLFlow, Airflow)
• Experience with Microsoft Agent Framework, Azure AI Agent Service
• Knowledge of vector databases (Pinecone, Weaviate, Qdrant, Milvus)
• Familiarity with guardrail and AI safety techniques (output filtering, prompt injection defense)
• Experience in distributed systems, event-driven architectures, and workflow engines
• Prior involvement in training, fine-tuning, or experimenting with foundation models

• Python
• APIs
• microservices
• debugging
• code review
• Generative AI

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