Senior/Lead/Chief AI Engineer

EPAM Systems; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

EPAM Systems is seeking engineers who love the craft and know how to use AI to move faster. You’ll solve real client problems, make architectural decisions, and help your team find better ways to build software.

In this role, you will take goals, understand problems, design approaches, implement features, and verify results across production code, testing, and reviews. If you are hands-on, curious, and capable of collaborating across disciplines, this is a great place to grow.

Qualifications

• Shipped and maintained production software.
• Ability to reason about system architecture and trade-offs.
• Experience debugging unfamiliar systems and communicating decisions.
• Hands-on experience with AI tooling and automation.

Responsibilities

• Translate goals into practical steps and plan execution.
• Design approaches, implement features, test, and review changes.
• Verify results and manage blockers to deliver production outcomes.
• Configure and operate coding-agent harnesses and integrations.

Skills

Software engineering

Architecture reasoning

Debugging

Code review

AI tooling

Tools

Claude Code

Codex

Cursor

Job description

You still get a kick out of understanding a tricky system, tracking down a stubborn bug, or shipping something people actually use. AI has made you more ambitious about what you can build. At EPAM, we’re looking for engineers who love the craft and know how to use AI to move faster. You’ll solve real client problems, make architectural decisions, and help your team find better ways to build software.

Responsibilities

Take a goal and make it happen. Understand the problem, challenge assumptions, choose an approach, and follow through. The result might be a production feature, a validated architecture, or an AI workflow that makes the team more effective Put coding agents to work. Use them to explore codebases, implement features, test, debug, and review changes. Decide what to delegate, where to intervene, and how to verify the result Make agents more useful. Configure and extend their harnesses: context, skills, specialized agents, hooks, tools, execution environments, and feedback loops. Connect them to real repositories and engineering workflows Help good ideas spread. Try things on real problems, measure what improves, and help teammates adopt approaches that work

Requirements

Engineering depth. You've shipped and maintained production software. You can write code, reason about architecture, debug unfamiliar systems, and explain your technical decisions Daily experience with coding agents. You're fluent with tools such as Claude Code, Codex, Cursor, or equivalents. You understand their capabilities, limitations, and failure modes Hands-on harness experience. You've configured and adapted the systems around an agent, using building blocks such as skills, specialized agents, hooks, and tool integrations. You understand context, execution loops, state, failure recovery, and how to verify completion A focus on outcomes. You turn an open-ended goal into practical steps, work through blockers, and bring others along. You take responsibility for the result, including code an agent wrote Building a coding-agent harness from scratch is a big plus. So are custom tools or MCP integrations, agent evaluations, and experience coordinating multiple agents