Senior/Lead/Chief AI Native Engineer

EPAM Systems; United States; A distancia; USD 120.000 - 160.000; Jornada completa

Descripción de la vacante

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.

You will work on goals, challenge assumptions, and apply coding agents to explore codebases, implement features, test, debug, and review changes, while configuring harnesses, contexts, and tools to improve outcomes.

Formación

• Shipped and maintained production software.
• Daily experience with coding agents and their limits.
• Configured and adapted agent systems with skills, hooks, and tools.
• Translate open-ended goals into concrete steps and results.
• Building a coding-agent harness from scratch is a big plus.

Responsabilidades

• 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 and 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 by configuring and extending their harnesses: context, skills, hooks, tools, and execution environments.
• Connect them to real repositories and engineering workflows, and help good ideas spread.

Conocimientos

Engineering depth

Coding agents

Harness experience

Outcome focus

Agent harness

Herramientas

ClaudeCode

Codex

Cursor

Descripción del empleo

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
• Dailyexperience with coding agents You're fluentwith tools such as ClaudeCode, Codex, Cursor, or equivalents You understand their capabilities, limitations, and failure modes
• Hands‑on harnessexperience 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 howto verify completion
• A focuson outcomes You turn an open‑ended goal into practical steps, work through blockers, and bring others along You take responsibilityforthe result, including code an agent wrote
• Buildingacoding‑agent harness from scratch is a bigplus Soare custom tools or MCP integrations, agent evaluations, and experience coordinating multiple agents