AI / ML 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.