Senior AI-Native Engineer

EPAM is global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and ambitious startups. With over thirty years of expertise in custom software, product and platform engineering, we empower our clients to become AI-Native enterprises, driving measurable value from innovation and digital investments.

• Minimum of 5 years of professional software engineering experience with a demonstrated focus on AI-powered systems
• Proven, hands-on experience in AI Agents development or LLM integration — building and shipping applications where large language models drive autonomous reasoning, content generation, or complex decision-making
• Experience with AI orchestration frameworks such as Spring AI or LangChain4J for structured development of agent-based applications
• Strong Prompt Engineering skills — including designing prompt templates, chains, and system instructions producing reliable, context-aware model behavior in production settings
• Proficiency in Python for AI/ML prototyping, model interaction, scripting, and working within the broader AI development ecosystem
• Hands-on experience with Amazon Web Services or Google Cloud Platform, particularly services supporting AI/ML workloads (e.g., Bedrock, SageMaker, Vertex AI, Lambda, Cloud Functions)
• Demonstrated practice of embedding AI into the SDLC — actively using AI-powered assistants, agents, or copilots to accelerate and enhance software development workflows
• Strong system design skills with the ability to architect solutions combining traditional services with autonomous AI components
• Critical thinking and ability to assess model limitations, hallucination risks, and appropriate use cases for AI automation
• Excellent communication skills and ability to work effectively in international, cross-functional Agile teams
• Proficiency in English (B2+), both written and spoken

Nice to have:

• Experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines to ground model responses in verified, domain-specific knowledge
• Familiarity with Vector Databases (e.g., Pinecone, Weaviate, pgvector, Chroma, Milvus) for semantic search, similarity matching, and embedding management
• Understanding of Model Context Protocol (MCP) for enabling standardized interaction between AI agents and external tools, APIs, or data sources
• Hands-on experience with Anthropic Claude Code or similar agentic developer tools for AI-native software engineering workflows
• Familiarity with Google Agent Development Kit (ADK) for building, testing, and deploying multi-agent systems with structured orchestration

• Architect and develop AI-native applications where intelligent agents and LLM-powered components serve as foundational building blocks
• Design and implement multi-agent systems capable of autonomous reasoning, planning, tool selection, and execution of complex workflows
• Integrate Large Language Models into production environments, ensuring reliability, observability, and graceful degradation
• Embed AI throughout the software development lifecycle — from AI-assisted design and code generation to intelligent testing, review, and deployment automation
• Develop orchestration layers that manage agent communication, shared memory, context windows, and task delegation
• Craft and iterate on prompt engineering strategies to drive consistent, high-quality model outputs across diverse use cases
• Evaluate and benchmark emerging AI models, frameworks, and agent platforms to inform technology decisions
• Design feedback loops, guardrails, and evaluation mechanisms to ensure AI system safety, accuracy, and alignment
• Collaborate with cross-functional teams — including product managers, data engineers, and platform architects — to deliver AI-driven solutions at scale
• Contribute to the creation of internal standards, reference architectures, and reusable patterns for AI-native development
• Mentor engineers across teams on agentic design patterns, LLM integration best practices, and AI-first thinking
• Continuously monitor the evolving AI landscape and translate emerging research into practical engineering approaches