Lead AI Enablement Engineer

** Lead AI Enablement Engineer
** Position Summary We are seeking a
** Lead AI Enablement Engineer
** to drive the adoption and evolution of AI-assisted engineering practices across product engineering teams. This is a hands-on individual contributor role within the Platform team, partnering closely with the Platform Lead to improve developer productivity, software quality, and delivery efficiency through practical AI enablement.

The ideal candidate combines strong software engineering fundamentals with deep, hands-on experience using AI coding tools, LLMs, and agent-based workflows. You will establish reusable AI-first development practices, integrate AI workflows into the software delivery lifecycle, and build the foundations for validated, sandboxed, and orchestrated agent execution.

You will also help shape reusable AI patterns for customer-facing product capabilities and internal employee tools, balancing innovation with security, governance, reliability, and measurable engineering outcomes.

Key Responsibilities AI-First Engineering Enablement
* Establish and scale AI-assisted workflows across the software development lifecycle, including planning, coding, debugging, testing, design, documentation, code reviews, and production investigation.
* Develop reusable agent instructions, prompts, skills, repository context, integrations, and reference implementations that engineering teams can adopt directly.
* Evaluate emerging AI coding tools, LLM platforms, and agent frameworks to identify opportunities to improve engineering productivity and software delivery.
* Help teams transition from ad hoc, locally executed AI usage toward structured, repeatable, and validated AI-assisted workflows.

Platform Integration and Agent Orchestration
* Partner with the Platform Lead to integrate AI workflows with CI/CD pipelines, automated testing, development sandboxes, observability, release readiness, and developer productivity tooling.
* Develop patterns for orchestrating multiple AI agents and parallel workflows, with appropriate human oversight and validation.
* Build integrations with developer tools, repositories, APIs, and internal systems to provide AI agents with relevant context and controlled access to resources.
* Contribute to the evolution of sandbox-based agent execution environments that support safe, reliable, and scalable engineering workflows.

Quality, Validation, and Governance
* Design validation loops that ensure AI-generated code and other outputs meet engineering standards through automated tests, code reviews, security checks, and release controls.
* Establish evaluation methods and quality signals to assess the accuracy, reliability, usefulness, and consistency of AI-assisted workflows.
* Apply sound engineering judgment to security, privacy, access control, auditability, operational risk, and AI usage costs.
* Collaborate with engineering and platform stakeholders to ensure AI adoption improves outcomes without compromising production reliability or software quality.

Product and Internal AI Capabilities
* Define reusable architectural patterns for AI-powered product features and internal employee capabilities.
* Support the development of AI assistants, retrieval-augmented generation (RAG) solutions, knowledge agents, workflow automation, and tool-enabled applications.
* Establish shared patterns for agent integrations, context management, permissions, governance, and reusable AI components.
* Partner with product and engineering teams to identify opportunities to embed AI into existing workflows and customer-facing capabilities.

Developer Enablement and Continuous Improvement
* Drive adoption through hands-on demonstrations, pairing sessions, workshops, documentation, examples, and engineering office hours.
* Coach engineers on effective AI-assisted development practices, agent orchestration, and output validation.
* Measure the impact of AI enablement using adoption, productivity, delivery speed, software quality, developer experience, and cost metrics.
* Continuously refine workflows based on engineering feedback, evaluation results, and measurable business outcomes.

Required Qualifications
* ** 7+ years of software engineering experience preferred**, with consideration for strong senior-level candidates who demonstrate exceptional hands-on AI engineering and enablement capabilities.
* Significant practical experience using AI tools for real-world software engineering, including AI coding assistants, agentic development workflows,…