Senior/Lead AI Solution Engineer

EPAM Systems; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

EPAM Systems Inc. seeks a Lead for AI-native software engineering teams. You will co-create how engineers collaborate with AI agents and own delivery quality while interfacing directly with client stakeholders.

You will drive problem framing, specs, and production delivery, shaping agent workflows, and ensuring robust code through TDD, reviews, and secure CI/CD.

Qualifications

• 7+ years in software engineering with hands-on experience in Java, .NET, or Python.
• 6+ months daily hands-on experience using AI coding agents on real projects.
• Strong context engineering for coding agents and MCP.
• Experience with spec-driven development and breaking work into agent-friendly tasks.
• Proven ability to maintain production-quality AI-generated code and CI/CD gates.
• Awareness of agent security risks including secrets and data leakage.

Responsibilities

• Drive technical delivery for a small AI-native team from problem framing to production.
• Design and refine the team's agentic SDLC and review gates.
• Configure repos for agent use, including prompts, skills, and MCP servers.
• Establish standards for validating AI-generated code, including TDD and test automation.
• Establish safe-use policies for agents covering permissions and client data.
• Track and improve AI cost efficiency, tokens, and model selection.
• Take ownership of solution architecture and mentor engineers, contributing to PoCs and presales.

Skills

Software engineering

Java/.NET/Python

AI coding agents

Context engineering

TDD & CI/CD

Security awareness

Leadership & mentoring

English proficiency (B2+)

Tools

GitHub Copilot agent mode

Claude Code

Codex

Job description

We're building small AI-native teams where AI coding agents serve as the default method for developing software—covering web apps, APIs, data pipelines, modernization efforts, and agentic systems for clients. This position focuses on engineering alongside agents, not just constructing them. As Lead, you'll establish how the team collaborates with agents, take ownership of delivery quality, and engage directly with client stakeholders.

Responsibilities

• Drive technical delivery for a small AI-native team, covering everything from problem framing and specifications through to production
• Design and refine the team's agentic SDLC, including spec-driven workflows, agent configuration, parallel and background agent tasks, and review and quality gates
• Configure repos for agent use, encompassing instructions, prompt files, skills, custom agents, MCP servers, along with the architecture, documentation, and tests needed for reliable agent performance
• Establish standards for validating AI-generated code, including TDD, test automation, code review, and static and security analysis within CI/CD
• Establish safe-use policies for agents covering permissions, secrets, client data, and approved tools
• Track and improve AI cost efficiency and delivery outcomes, including tokens and premium requests, model selection, cycle time, and rework
• Take ownership of solution architecture, mentor engineers on agentic practices, and contribute to PoCs, estimates, and presales efforts

Requirements

• 7+ years in software engineering, with hands-on experience in Java, .NET, or Python and strong design and debugging capabilities
• 6+ months of daily, hands-on experience using AI coding agents (GitHub Copilot agent mode / CLI / coding agent, Claude Code, or Codex) on real projects
• Strong expertise in context engineering for coding agents, including repo instructions, prompt files, skills, custom agents and subagents, and MCP
• Hands-on experience with spec-driven development, including the ability to break work into tasks suited for agents
• Demonstrated methods for maintaining production-quality AI-generated code, including TDD, test automation, reviewing large AI diffs, and CI/CD quality gates
• Awareness of agent security risks, including permissions, secrets, data leakage, and prompt injection through repos and tools
• Practical understanding of AI cost management, including model selection per task, token and premium-request economics, and measuring productivity impact
• Solid solution architecture skills, along with experience leading and mentoring engineers and collaborating with clients
• English proficiency at B2+ level
• Nice to have Experience building LLM or agentic applications, including agent frameworks, RAG, tool calling, evals, guardrails, and LLM observability
• Experience rolling out AI-assisted development to other teams, including enablement, playbooks, and metrics
• Cloud and AI certifications (Azure, AWS, GCP, GitHub Copilot, Anthropic)
• A business analysis mindset, along with consulting or presales experience, UX/UI knowledge, or financial services domain expertise