Senior AI Engineer: LLM Architecture & RAG Pipelines

EPAM Systems; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

EPAM Systems seeks a senior engineer to architect and ship AI features such as natural-language query, summarization, and intelligent validation across products. You will establish reusable patterns for retrieval and guardrails, and drive RAG workflows with embeddings and citations.

You will lead a small team on LLM integration, define evaluation methods, and ensure cost, latency and reliability through caching and telemetry. Strong AI governance is required.

Qualifications

• 8+ years of software engineering experience in .NET (C#) and/or Python with lead-level ownership of AI feature architecture across one or more products.
• Proven background in shipping LLM-based features to production, including tool and function calling, structured outputs and streaming.
• Proficiency with Azure OpenAI / Azure AI Foundry, OpenAI or Anthropic APIs.
• Solid SQL fundamentals and API integration skills.

Responsibilities

• Own the architecture of AI features and ship them, including NLP queries, summarization and intelligent validation.
• Establish reusable AI feature patterns for retrieval, evaluation and guardrails to avoid reinvention.
• Drive end-to-end RAG workflows with embeddings, hybrid search and citations.
• Lead and mentor a small engineering team on LLM integration and evaluation.
• Define evaluation discipline with golden datasets and regression checks.

Skills

AI feature architecture

LLM integration

Azure OpenAI

Python

C#

Agentic automation

Lead ownership

Tools

Azure OpenAI API

OpenAI API

Anthropic API

GitHub Copilot

Job description

EPAM Systems seeks a senior engineer to architect and ship AI features such as natural-language query, summarization, and intelligent validation across products. You will establish reusable patterns for retrieval and guardrails, and drive RAG workflows with embeddings and citations.

You will lead a small team on LLM integration, define evaluation methods, and ensure cost, latency and reliability through caching and telemetry. Strong AI governance is required.