AI Native Engineer

EPAM Systems; United States; Remote; USD 180,000 - 210,000; Full time

Job summary

EPAM Systems Inc. is expanding AI-native teams focused on building AI-powered software across web apps, APIs, data pipelines, modernization and agentic systems. As Lead, you own delivery quality, define team workflows with AI agents, and collaborate directly with client stakeholders.

You will guide a small team, establish engineering standards for agent usage, and mentor engineers while contributing to presales and PoCs. Strong Java/.NET/Python and real-world agent experience are essential.

Qualifications

• 7+ years in software engineering; hands-on with Java, .NET or Python; strong design and debugging skills.
• 6+ months daily, hands-on use of AI coding agents on real projects.
• Deep context engineering for coding agents: repo instructions, prompt files, skills, custom agents.
• Experience with TDD, test automation, CI/CD quality gates for AI-generated code.

Responsibilities

• Lead technical delivery of a small AI-native team from problem framing to production.
• Define agentic SDLC: specs, configuration, and background/parallel agent work.
• Set up repositories for agents: instructions, prompt files, skills, custom agents, MCP servers.
• Define how AI-generated code is checked: TDD, test automation, code review, static and security analysis in CI/CD.

Skills

Java

C#.NET

Python

Design & Debugging

AI coding agents usage

Tools

GitHub Copilot

Codex

CLAUDE Code

CI/CD

Job description

We're forming small AI-native teams where AI coding agents are the default way to build software, whether that's web apps, APIs, data pipelines, modernization or agentic systems for clients. This role is about engineering with agents, not only building them. As Lead, you define how the team works with agents, own delivery quality, and work directly with client stakeholders.

Responsibilities

Lead technical delivery of a small AI-native team, from problem framing and specs to production Design and evolve the team's agentic SDLC: spec-driven workflow, agent configuration, parallel and background agent work, review and quality gates
Set up repos for agents: instructions, prompt files, skills, custom agents, MCP servers, plus the architecture, docs and tests that let agents work reliably
Define how AI-generated code is checked: TDD, test automation, code review, static and security analysis in CI/CD
Set safe-use rules for agents: permissions, secrets, client data, approved tools
Measure and optimize AI cost and delivery impact (tokens and premium requests, model choice, cycle time, rework)
Own the solution architecture; mentor engineers in agentic practices; contribute to PoCs, estimates and presales

Requirements

7+ years in software engineering, with hands-on Java, .NET or Python, and strong design and debugging skills 6+ months of daily, hands-on use of AI coding agents (GitHub Copilot agent mode / CLI / coding agent, Claude Code or Codex) on real projects Deep context engineering for coding agents: repo instructions, prompt files, skills, custom agents and subagents, MCP Spec-driven development in practice, including breaking work into tasks an agent can do well Proven practices for keeping AI-generated code at production quality: TDD, test automation, reviewing large AI diffs, CI/CD quality gates Understanding of agent security risks: permissions, secrets, data leakage, prompt injection through repos and tools Practical AI cost awareness: model selection per task, token and premium-request economics, measuring productivity impact Solution architecture skills; experience leading and mentoring engineers and working with clients English level B2+ Nice to have Experience building LLM or agentic applications (agent frameworks, RAG, tool calling, evals, guardrails, LLM observability) Showcase of rolling out AI-assisted development to other teams (enablement, playbooks, metrics) Cloud and AI certifications (Azure, AWS, GCP, GitHub Copilot, Anthropic) Business analysis mindset with consulting or presales experience Familiarity with UX/UI and the financial services domain