[Hiring] Lead AI-Native Engineer @EPAM Systems

Role Description

We are seeking a Lead AI-Native Engineer to raise a team's or customer's AI maturity and prove it with numbers. This role designs and ships AI systems, with success measured by what changed for others:

• Engineers who now maintain their own skills
• A customer team that moved from pilot to production
• Adoption and quality metrics that improved because of how the work was introduced

Responsibilities:

• Lead AI adoption for a squad or customer team: choose the first wins, handle resistance (vendor teams, sceptical senior engineers, delivery pressure), and build trust before changing process
• Design the delivery pipeline and its human gates, choose the model mix, MCP servers, tool sets and skill registry, and set the tool-restriction policy
• Track adoption and quality metrics (acceptance rate, PR cycle time, defect density, AI-touched PR ratio), tune the workflow from them, and report outcomes to leadership
• Set how the team or product tracks and controls AI cost: cost per task or outcome, budgets and alerts
• Coach engineers up a level and review the skills and agents they create
• Represent the AI delivery model to client and business stakeholders: explain trade-offs and hold the delivery model under scope pressure
• Document the operating model so a peer can run it without you
• Harvest reusable skills, patterns and reference architectures back to the wider organization
• Keep a documented path towards more autonomous delivery, and decide which steps are safe to remove human review from
• Keep designing and shipping AI systems: multi-stage LLM pipelines and agents with a curated tool set, backed by evaluation

Qualifications

• 8+ years of professional software engineering experience
• 3+ years of hands-on experience with LLM or agent systems
• 2+ years in a lead, architect, mentoring or customer-adoption capacity
• Showcase of moving 3 or more engineers up a level, or moving a customer team from pilot to production use, with numbers to show it
• Background in a production AI system with quantitative outcomes (accuracy, cycle time, cost, hours saved, adoption), the failure modes encountered, and the structural fixes
• Expertise in AI work that runs without daily attention, multi-step LLM or agent design with tool restriction and schemas, an evaluation approach, and structural fixes for wrong output
• Understanding of trade-offs named: what was chosen, what was rejected and why, ideally across more than one project
• Track record in adoption: first wins, metrics, tuning, and how resistance was handled
• Proficiency in a cost-control approach that others follow, including a measured reduction that kept quality
• Familiarity with a documented operating model that a peer can run without the candidate
• Skills in clear communication with outcomes and lessons from real situations, including with skeptical audiences

Requirements

• Nice to have: Background in reusable patterns adopted by other squads or engagements, and multi-agent topologies designed and run in production, with observability (token spend, hallucination rate, false-positive ratio, retry counts)
• Experience in governance and compliance for AI (guardrail policies, approval flows, audit trails)
• Familiarity with pre-sales or client-facing work
• Understanding of autonomous delivery pipelines, with concrete next steps
• Showcase of conference talks, internal courses or written guidance on AI adoption