ai engineer for AI-native applications

EPAM Systems develops enterprise software products, open source solutions, and accelerators.

• 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 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;
• Monitor the evolving AI landscape and translate emerging research into practical engineering approaches.

• Have at least 5 years of professional software engineering experience with a demonstrated focus on AI-powered systems;
• Have 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;
• Have experience with AI orchestration frameworks such as Spring AI or LangChain4J;
• Have strong Prompt Engineering skills, including designing prompt templates, chains, and system instructions for reliable, context-aware model behavior in production;
• Be proficient in Python for AI/ML prototyping, model interaction, scripting, and work within the broader AI development ecosystem;
• Have hands-on experience with AWS or GCP cloud platforms, particularly AI/ML services such as Bedrock, SageMaker, Vertex AI, Lambda, or Cloud Functions;
• Have experience embedding AI into the SDLC through AI-powered assistants, agents, or copilots;
• Have strong system design skills and the ability to architect solutions combining traditional services with autonomous AI components;
• Demonstrate critical thinking and the ability to assess model limitations, hallucination risks, and appropriate use cases for AI automation;
• Have excellent communication skills and the ability to work effectively in international, cross-functional Agile teams;
• Have English proficiency at B2+ level, both written and spoken;
• Nice to have: Experience designing and implementing RAG pipelines, familiarity with Vector Databases such as Pinecone, Weaviate, pgvector, Chroma, or Milvus, understanding of Model Context Protocol (MCP), hands-on experience with Anthropic Claude Code or similar agentic developer tools, familiarity with Google Agent Development Kit (ADK).

• Hybrid work model with 3 days per week from an EPAM office.