Lead AI Enablement Engineer

Description

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, and LLM-powered developer tools.
• Hands-on experience with tools such as Claude Code, Warp, or comparable AI coding and engineering platforms.
• Strong software engineering fundamentals, including application development, testing, code review, debugging, and production support.
• Experience building reusable developer workflows, integrations, automation, agent instructions, prompt patterns, skills, or reference implementations.
• Working knowledge of LLM APIs, tool calling, agent orchestration, and Model Context Protocol (MCP) or comparable integration approaches.
• Understanding of modern software delivery practices, including Git, CI/CD, automated testing, release management, and production readiness.
• Ability to evaluate AI-generated outputs critically and implement appropriate testing, review, security, and validation mechanisms.
• Strong communication and collaboration skills, with the ability to influence engineering practices and drive adoption across teams.
• A practical, AI-first mindset focused on measurable improvements in engineering productivity, delivery quality, and developer experience.

Technology and Tools

Experience with several of the following technologies and capabilities is highly desirable:

AI Engineering and Agent Development

• Claude Code, Warp, AI coding agents, and LLM-powered developer workflows.
• Agent SDKs and APIs from Anthropic, OpenAI, AWS Bedrock, or comparable platforms.
• MCP servers, tool calling, agent orchestration, reusable instructions, prompt engineering, and agent skills.

Software Engineering and Developer Productivity

• Proficiency in at least one modern programming language, such as Python, TypeScript, Go, C#, or .NET.
• Git-based development workflows, CI/CD pipelines, test automation, code review, and release management.
• Developer productivity platforms, internal tooling, and engineering workflow automation.

AI Applications and Knowledge Systems

• Retrieval-augmented generation (RAG), semantic search, embeddings, vector databases, and knowledge agents.
• AI assistants, workflow automation, and integrations with internal systems and collaboration tools.

Cloud, Infrastructure, and Observability

• AWS or comparable cloud platforms, containers, and sandboxed execution environments.
• Logging, tracing, monitoring, observability, AI evaluation, and cost tracking.
• Security controls, identity and access management, privacy, auditability, and governance for AI systems.

Preferred Qualifications

• Experience implementing sandboxed or cloud-based agent execution and parallel AI workflows.
• Familiarity with human-in-the-loop orchestration and validation of autonomous or semi-autonomous agents.
• Experience building AI-powered SaaS features, internal knowledge-base agents, Slack integrations, or employee AI assistants.
• Experience modernizing legacy applications or engineering workflows in a mature production SaaS environment.
• Domain experience in fintech, payments, accounting, eCommerce, property management, or other transactional businesses.
• Experience working in security-conscious, compliance-driven, or regulated environments.

Success Metrics

Success in this role will be measured by the ability to:

• Establish repeatable, AI-first engineering workflows that product teams can adopt across common development activities.
• Demonstrate measurable improvements in engineering productivity, delivery speed, software quality, review confidence, and developer experience.
• Improve the relevance and reliability of AI-generated outputs through better repository context, business rules, engineering standards, and validation.
• Enable engineers to orchestrate AI-driven workflows with clear testing, review, security, and release-readiness controls.
• Build a strong technical foundation for sandbox-based execution and parallel agentic development.
• Establish reusable, governed patterns for AI assistants, retrieval, automation, and internal and customer-facing AI capabilities.
• Demonstrate tangible value through adoption, quality, productivity, delivery, and cost metrics.

Work Model and Location

We are based in the New York metropolitan area and hire across the U.S. East Coast, with working hours aligned to New York time.

We value in-person collaboration while recognizing the importance of flexibility and where our employees choose to live.

• Hybrid: Candidates located near the New York metropolitan area are expected to work from the office two to three days per week.
• Remote-friendly: Candidates located farther away on the U.S. East Coast can work primarily remotely, with in-person visits approximately four to six times per year.
• Onboarding: Regardless of location, additional in-person collaboration is expected during the initial onboarding period.

This role offers the opportunity to shape how AI transforms software engineering practices across product teams while building practical, scalable solutions that deliver measurable results.