Solutions Architect (AI Native SDLC)

EPAM Systems is a leading global provider of digital platform engineering and software development services. We help global enterprises innovate, build and transform their core businesses through technology.

Join our AI-Centric Delivery practice as a Solutions Architect . You will design and deliver enterprise solution architectures where AI serves as a foundational engineering capability. Combine your architectural expertise with hands-on application to build AI-augmented software development lifecycle (SDLC) workflows, agentic systems and LLM-powered delivery tooling.

This execution-oriented role empowers you to define technical direction, validate architectures through personal prototyping and work alongside engineering teams during implementation to drive real impact.

Design, build and validate AI-SDLC developer agents and multi-agent orchestration workflows focusing on automation and engineering throughput Advise senior client stakeholders by translating business requirements into AI-augmented solution architectures Communicate design trade-offs across latency, cost, observability and risk Architect and integrate AI-enabled workflows across the engineering stack including version control, CI/CD pipelines, code review, testing and documentation Deliver functional prototypes within tight delivery windows to demonstrate the value of AI-native engineering approaches Lead the end-to-end design of enterprise solution architectures incorporating agentic systems, LLM-powered workflows and RAG pipelines Collaborate with engineering leads, product owners and enterprise architecture teams to align solution designs with security, governance and integration requirements Proven track record as a senior software engineer or solutions architect with successful delivery across complex enterprise-scale engagements Hands-on expertise with large language models and generative AI (such as Anthropic Claude, OpenAI GPT or Google Gemini) Demonstrated capability in prompt engineering, model selection, context management, cost and latency optimization in production environments Background in designing and implementing agentic workflows involving tool use, memory systems, multi-step reasoning and human-in-the-loop patterns Solid foundation in enterprise architecture fundamentals including cloud platforms (AWS, Azure or GCP), microservices, API design, data architecture and integration patterns Clear communication practices for navigating strategic design and hands-on implementation to validate architectural decisions through working prototypes Familiarity with multi-agent orchestration frameworks like CrewAI, AutoGen or LangGraph Knowledge of LLM evaluation, guardrails and observability tooling like LangSmith or Arize Practical understanding of AI development frameworks including LangChain, LlamaIndex or Hugging Face Exposure to vector database technologies like FAISS, Pinecone, Qdrant, Chroma or Weaviate Experience deploying AI-assisted code generation tooling at an organizational scale Background in enterprise integration platforms, event-driven architecture or data mesh