Director of Engineering

Lazer

2d•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.