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.
Responsibilities
- 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
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
Nice to have
- 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
