AI Engineering Team Lead (Forward Deployed)

Lazer Core is a specialized team within Lazer focused on building robust, AI-powered full-stack applications. We partner closely with ambitious teams across industries to bring cutting-edge products to life, leveraging the latest in AI, agentic workflows, and scalable infrastructure. Our engineers work across the stack, helping clients solve high-impact problems with tailored, production-ready solutions.

Who You Are:

• 10+ years of engineering experience with 3+ years directly managing senior engineers, including hiring, performance management, and the difficult conversations that come with both.

• Experience leading teams of 15+ engineers, ideally spread across multiple concurrent projects or engagements.

• Still hands-on technically: you can read an architecture, spot the flaw, and propose a better approach.

• Strong full stack depth with TypeScript, React, Node.js, Python or Go.

• Proven experience building and shipping production agentic applications, including orchestration and tool-calling patterns.

• Confidence setting up evaluation harnesses and testing infrastructure around agentic applications, run against real prompts and business metrics.

• Familiarity with MLOps or LLMOps practices, including monitoring, guardrails, and human-in-the-loop design.

• Comfort working with at least one major cloud provider (GCP, AWS, Azure).

• AI-native in your own daily practice, with grounded opinions on coding agents, harnesses, and workflows that you can teach credibly to other engineers.

• Previous experience in consulting, agency, or forward deployed engineering roles, with the ability to manage client expectations and timelines.

• A track record of developing people deliberately through training programs, onboarding paths, or mentorship structures that outlasted your direct involvement.

• Comfort operating in ambiguity, making calls without waiting for permission, and creating clarity for the people around you.

• Exceptional communication skills across executives, staff engineers, and written updates, and the instinct to hold your team to the same standard.

What You'll Do:

• Co-own the AI engineering team of roughly 30 forward deployed engineers alongside existing engineering leaders.

• Own the health and happiness of the team through regular 1:1s, catching disengagement, burnout, and bench frustration before they become resignations.

• Run performance reviews against our FDE rubric, which weighs consulting skills as equally important as technical skills.

• Level up the team as Forward Deployed Engineers through training sessions, playbooks, checklists, pairing, and internal writeups.

• Define and maintain the baseline AI competency every engineer on the team is held to, and keep it current as tooling changes.

• Codify what our best engineers do instinctively into shared, documented practice, and distill field learnings back into reusable patterns.

• Track engagement health across the portfolio, surfacing delivery risk, uncovered areas, and deteriorating client relationships early.

• Review architectures and pressure-test approaches so they hold up to scrutiny from a client CTO or architecture review board.

• Provide project and technical guidance on evaluation strategy, instrumentation, and how we prove impact.

• Step onto engagements as technical lead when a project needs it.

• Partner with talent on sourcing, run hiring manager screens, calibrate interviewers, and own the technical bar for the team.

• Staff engagements by matching engineers to projects on skill, growth, and client fit, and manage workload so utilization and development stay in balance.

• Support scoping and pre-sales conversations where technical credibility matters, and act as senior escalation point for clients and partners.

Nice to Have:

• Enterprise depth in financial services, healthcare, or retail, including how security, legal, and compliance review actually work.

• Experience scaling a forward deployed engineering, solutions engineering, or professional services team.

• Experience running AI enablement or developer productivity programs for an engineering org.

• Background as a founder, CTO, or early engineer at a venture-backed startup.

• Experience fine-tuning LLMs using open-source models or closed-source services.

• Familiarity with Kubernetes and container orchestration.

• Experience designing scalable data pipelines or data lakes.

• Relevant certifications, open-source contributions, or technical publications.