Lead AI-Native Engineer

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AI Native Engineering

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, and adoption and quality metrics that improved because of how the work was introduced.

Чим ви будете займатися у цій ролі

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

Навички

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

Буде перевагою

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