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. Responsibilities 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 Requirements Extensive engineering background as a high-performing developer with mastery of Java, JavaScript, Python or .NET 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 Nice to have 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