Lead AI Engineer

Responsibilities:
Define and drive the AI and DevX roadmap across the engineering organization, aligned to business priorities and platform realities.
Build internal engineering leverage through better CI/CD, local development workflows, developer tooling, templates, golden paths, and self-serve platform capabilities.
Identify high-value opportunities where AI can improve engineering throughput and quality: code assistance, test generation, debugging support, documentation, runbooks, code review, incident analysis, and engineering knowledge access.
Create practical, safe patterns for AI usage in software development, including evaluation, guardrails, observability, security, and responsible rollout.
Improve the everyday developer journey from idea to production: setup time, build/test speed, release confidence, deployment ergonomics, and production feedback loops.
Partner with engineering leaders to remove recurring friction across squads and standardize what should be standardized, without slowing down product delivery.
Drive engineering productivity through measurable improvements in lead time, deployment reliability, CI stability, change failure rate, code review turnaround, and onboarding effectiveness.
Raise the bar on technical quality by influencing architecture, tooling choices, automation strategy, and platform standards.
Act as a senior hands-on engineer: prototype, build, review, unblock, and occasionally get into the weeds when the problem deserves it.
Coach engineers and leaders on how to work effectively with AI, platform capabilities, and scalable engineering practices.
Requirements:
5+ years of software engineering experience, with strong depth in backend or platform engineering and enough breadth to work across tooling, systems, and delivery workflows.
Proven experience improving developer experience, engineering productivity, platform standards, or internal tooling in a fast-moving product or platform environment.
Hands-on familiarity with modern AI tooling, agentic development, and workflows for software engineering, along with the judgment to separate real leverage from shiny demos.
Strong software design and systems thinking; able to connect developer ergonomics with architecture, reliability, security, and scale.
Good understanding of CI/CD, testing strategy, release engineering, observability, and developer platforms.
Ability to influence across teams without relying on hierarchy; this role needs builders who can persuade, simplify, and make change stick.
Strong written and verbal communication. You should be able to turn messy engineering pain into a clear plan and then into working systems.
Bias for action, ownership, and pragmatic problem solving. We value builders who improve the path, not just describe the potholes.
Good to have:
Experience with internal developer platforms, workflow automation, policy-as-code, or platform engineering at scale.
Experience building or operationalizing LLM-based workflows, agentic systems, evaluation loops, retrieval systems, or AI copilots for internal use cases.
Exposure to fintech, commerce, or high-scale consumer/merchant platforms.
Experience mentoring senior engineers and acting as a technical multiplier across multiple teams.
Familiarity with cloud-native systems, infrastructure-as-code, and security-minded engineering practices.

Experience
6-10 yrs