Key Skills: Gen AI Solutions Development, Large Language Models (LLM),
Cloud Infrastructure Provisioning, Multi-Agent Systems Evaluation and
Testing, Systems Architecture
Job Description
EPAM Systems is a leading global provider of digital platform engineering
software development services. We help global enterprises innovate,
build transform their core businesses through technology.
Join our AI-Centric Delivery practice as a Solutions Architect. You will design
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 LLM-powered delivery
tooling.
This execution-oriented role empowers you to define technical direction,
validate architectures through personal prototyping work alongside
engineering teams during implementation to drive real impact.
Responsibilities
Design, build validate AI-SDLC developer agents multi-agent
orchestration workflows focusing on automation engineering
throughput.
Advise seniclient stakeholders by translating business requirements
AI-augmented solution architectures.
Communicate design trade-offs across latency, cost, observability and
risk.
Architect integrate AI-enabled workflows across the engineering
stack including version control, CI/CD pipelines, code review, testing
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 RAG
pipelines.
Collaborate with engineering leads, product owners enterprise
architecture teams to align solution designs with security, governance
integration requirements.
Requirements
Proven track record as a senisoftware engineer solutions architect
with successful delivery across complex enterprise-scale engagements.
Hands-on expertise with large language models generative AI
(such as Anthropic Claude, OpenAI GPT Google Gemini).
Demonstrated capability in prompt engineering, model selection,
context management, cost latency optimization in production
environments.
Background in designing implementing agentic workflows
involving tool use, memory systems, multi-step reasoning humanin-the-loop patterns.
Solid foundation in enterprise architecture fundamentals including
cloud platforms (AWS, Azure GCP), microservices, API design, data
architecture integration patterns.
Clear communication practices fnavigating strategic design and
hands-on implementation to validate architectural decisions through
working prototypes.
Nice to Have
Familiarity with multi-agentchestration frameworks like CrewAI,
AutoGen LangGraph.
Knowledge of LLM evaluation, guardrails observability tooling like
LangSmith Arize.
Practical understanding of AI development frameworks including
LangChain, LlamaIndex Hugging Face.
Exposure to vectdatabase technologies like FAISS, Pinecone, Qdrant,Chroma Weaviate.
Experience deploying AI-assisted code generation tooling at an
organizational scale.
Background in enterprise integration platforms, event-driven
architecture data mesh.
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