Applied Senior Software Engineer (AI Native Development)

EPAM Systems is a global team of technologists who design develop and deliver software that powers the world. We are at a pivotal moment where AI is the core engine of our engineering craft.

Join our AI-Centric Delivery team as a Senior Software Engineer to redefine the Software Development Lifecycle (SDLC). Go beyond writing code to design and orchestrate AI agents and agentic workflows that accelerate development velocity.

If you are a tool-agnostic engineer focused on the future of AI-native engineering this role offers the opportunity to build test and ship transformative technical solutions for global brands.

• Design and build sophisticated AI developer agents capable of generating refactoring and documenting complex codebases at scale
• Champion the shift from manual coding to an agentic workforce while maintaining hands-on technical involvement for high-level precision
• Deliver functional high-velocity prototypes in tight windows to prove the real-world power of AI-driven engineering
• Design and integrate AI-enabled workflows directly into the engineering stack including Git Jira and CI/CD pipelines to automate processes and maximize developer throughput
• Consult with stakeholders to translate complex business requirements into AI-SDLC augmented technical solutions
• Articulate the trade-offs of agentic design and ensure alignment with enterprise goals

• Extensive engineering background as a high-performing developer with mastery of Java JavaScript Python
• Hands-on expertise with large language models including Anthropic Claude OpenAI GPT or Google Gemini
• Proven capability in agentic workflows featuring tool use memory and multi-step reasoning focused on engineering tasks
• Strong understanding of RAG architectures prompt engineering and the mechanics of AI integration into production-grade SDLC workflows
• Execution-focused mindset with a strong preference for build test and ship of working solutions

• Experience with multi-agent orchestration frameworks like CrewAI or AutoGen
• Knowledge of vector databases such as FAISS Pinecone Qdrant or Chroma
• Familiarity with LLM evaluation guardrails and observability
• Cloud deployment experience with AWS Azure or GCP
• Practical experience with AI frameworks such as LangChain Hugging Face or LlamaIndex