Solutions Architect (AI Native SDLC)

Lead

Job description

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

Required skills

documentation security governance generative ai ai sdlc aws automation prompt engineering product owners testing gcp azure version control large language models cloud platforms ci/cd pipelines microservices observability api design data architecture code review prototyping risk cost cost optimization langgraph senior software engineer event-driven architecture data mesh llamaindex autogen solutions architect engineering teams integration patterns langchain rag pipelines crewai business requirements hugging face faiss chroma pinecone openai gpt observability tooling latency multi-step reasoning weaviate anthropic claude agentic workflows production environments technical direction architectural expertise guardrails langsmith architectural decisions enterprise integration platforms google gemini tool use qdrant agentic systems ai-enabled workflows latency optimization model selection llm evaluation strategic design senior client stakeholders context management integration requirements hands-on implementation engineering leads digital platform engineering arize working prototypes memory systems ai native sdlc software development services enterprise solution architectures foundational engineering capability ai-augmented software development lifecycle llm-powered delivery tooling ai-sdlc developer agents multi-agent orchestration workflows engineering throughput design trade-offs engineering stack functional prototypes ai-native engineering approaches enterprise architecture teams enterprise-scale engagements human-in-the-loop patterns enterprise architecture fundamentals multi-agent orchestration frameworks ai development frameworks vector database technologies ai-assisted code generation tooling

Solutions Architect (AI Native SDLC)

Job description

Responsibilities

Requirements

Nice to have

Required skills

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