Engineering Manager — AI-First Platform & Agent Teams

Platform & Product Engineering — LumiMeds
Location: Remote — partial US hours overlap required (minimum 4–5 hours daily overlap with US Pacific/Eastern)
Seniority: Manager — Player/Coach

What We're Building

LumiMeds is a high-growth telehealth platform building the operating system for modern virtual care — an AI-accelerated clinical engine, a high-converting e-commerce storefront, a consumer mobile app, and intake infrastructure that moves at the speed our patients expect.

We don't scale engineering by adding headcount. We scale by multiplying leverage — through better systems, better tooling, and better use of AI. Our engineers already orchestrate Claude agent teams in production. Your job is to raise the ceiling on what that means.

The Role

This is a player/coach role for an engineer-turned-manager who is serious about AI-native engineering. You will lead a team of 4–8 full-stack engineers and simultaneously operate as the team's resident expert in designing and running Claude agent teams — using coordinated AI agents as a structural force multiplier on engineering output.

You are not here to run standups and update Jira. You are here to build a team that ships more, faster, at higher quality than any team its size should be able to — by treating AI agent orchestration as a first-class engineering discipline. You will spend roughly 40% of your time coding and in agent pipelines, and 60% managing, designing systems, and raising the team's AI literacy.

What Makes This Role Different

Most engineering managers run teams of humans. You will run a team of humans and a fleet of agents — and your ability to design, direct, and verify agent work is as important as your ability to manage people.

We are not looking for someone who has used Claude or experimented with agents. We need someone who has built real agent pipelines under production pressure — who knows exactly where they break, how to recover, and how to design for reliability at scale. This is the core of the role.

• Break complex engineering initiatives into agent-executable subtasks with crisp acceptance criteria
• Design multi-agent pipelines — parallel subagents for feature branches, test generation, code review, and documentation — and stitch their outputs into production-ready deliverables
• Set the team's standards for when to use agents, how to prompt them effectively, how to verify their outputs, and when to override them
• Treat agent output as team output — you are accountable for everything the agents on your team produce
• Continuously raise the ceiling: as the models improve, you update the playbook

What You'll Actually Do

Lead and grow a high-performing engineering team. Hire, onboard, coach, and develop 4–8 engineers. Set clear expectations, give direct feedback, and build a culture where velocity and quality are not in tension.

Design and operate Claude agent teams. Architect agent pipelines using the Claude Agent SDK to parallelize engineering work at scale — parallel feature development, automated test coverage, documentation generation, code review passes, and spec-to-implementation workflows. You know how to define subagent roles, manage inter-agent context handoffs, validate outputs, and escalate to human judgment at the right moments.

Stay in the code. You are a working engineer. You write production code, review PRs with technical depth, debug hard problems, and pair with engineers on the work that needs a senior eye. AI augments your output — it does not replace your technical judgment.

Set the AI velocity standard. Define how the team uses AI tooling — Cursor, Claude Code, agent pipelines — and push the frontier. You have strong, specific opinions about how to prompt effectively, which tasks to delegate to agents, and how to verify agent output before it ships.

Own delivery end to end. Run sprint planning, resolve blockers, manage dependencies across product, clinical, and infrastructure. You own outcomes — not just process.

Write tickets AI agents can execute. Your specs are precise, structured, and unambiguous — acceptance criteria, edge cases, API contracts, all present. Claude Code or a junior engineer can run with them without a sync. Your specs don't loop.

Build and own consumer app and e-commerce systems. You've shipped full-stack consumer products end to end — mobile-backed apps, e-commerce storefronts, subscription billing, checkout flows. You understand high-conversion funnel architecture and have built or owned user behavior tracking infrastructure: event schemas, analytics pipelines, conversion funnels, retention dashboards. This is not adjacent to the role — it is the role.

Build and own A/B testing infrastructure. Design and maintain the experimentation platform that powers product decisions — feature flags, experiment assignment, statistical significance tracking, and results dashboards. You've built this before for high-traffic web products. You understand holdout groups, novelty effects, and how to run clean experiments across checkout flows, onboarding, and clinical intake.

Build the systems that make the team scale. Engineering standards, PR review norms, deployment practices, observability, incident response. You build the scaffolding once so the team doesn't rebuild it repeatedly.

Collaborate cross-functionally. Partner with Product, Clinical, and Ops to translate requirements into engineering reality. You are the technical voice in roadmap conversations — not a scheduler, but a decision-maker.

What You'll Own

Domain	Scope
Engineering Team	4–8 full-stack engineers — hiring, performance, growth
Agent Team Operations	Claude agent pipelines for parallelized engineering work
Delivery	Sprint execution, unblocking, cross-functional coordination
Technical Standards	Code quality, PR norms, AI tooling standards, observability
Core Platform	Clinical engine, e-commerce, subscription billing, mobile backend
A/B Testing Platform	Experimentation infrastructure, feature flags, statistical tracking
AI Infrastructure	LLM integration patterns, agent orchestration architecture

Required Skills & Experience

Engineering Leadership:

• 6+ years of software engineering experience, including 2+ years in a lead or management role
• Hands-on experience with Node.js / TypeScript backends and Next.js / React frontends — you can read, write, and review production code at a senior level
• Strong database fundamentals: PostgreSQL (schema design, migrations, query optimization), Redis

AI-Native Engineering (Non-negotiable):

• Claude Agent SDK: Demonstrated experience building and orchestrating multi-agent pipelines — decomposing tasks, defining subagent roles, managing context handoffs, validating agent output
• LLM Integration: Production experience integrating LLMs into real systems — streaming, tool use, structured outputs, prompt engineering
• AI Dev Tooling: Daily use of Claude Code, Cursor, or equivalent. You have built workflows around these tools, not just used them ad hoc
• You can articulate — with specificity — how agent orchestration changes what a small engineering team can ship

Experimentation & A/B Testing:

• Proven experience designing and building web A/B testing platforms from the ground up — not just using third-party tools, but owning the infrastructure
• Deep understanding of experiment design: randomization, assignment consistency, statistical power, holdout groups, and avoiding novelty bias
• Experience running experiments across high-traffic consumer funnels (checkout, onboarding, pricing, landing pages)
• Familiarity with feature flag systems (LaunchDarkly, Statsig, homegrown) and experimentation analytics pipelines

Consumer Product & Tracking:

• Hands-on experience building consumer apps and e-commerce platforms end to end — storefronts, checkout, subscriptions, billing
• Built user behavior tracking infrastructure: event schemas, analytics pipelines, conversion funnels, retention analysis
• Familiarity with tools like Segment, Mixpanel, Amplitude, or equivalent homegrown tracking systems

Systems & Delivery:

• Experience running engineering sprints, managing dependencies, and owning delivery timelines
• Ability to write engineering specs that AI coding agents and engineers can execute with minimal back-and-forth
• Familiarity with AWS (EC2, RDS, Lambda, S3), Vercel, GitHub Actions, and CI/CD pipelines

Compliance:

• Working knowledge of HIPAA/SOC2 requirements — you understand how compliance shapes architecture decisions

English:

• Fluent written and spoken English — all team communication is async in English (Slack, PRs, specs, docs)

Candidate Qualifications

• The player/coach instinct: You want to manage, but you're not ready to stop building. You think leaving code entirely would make you a worse manager.
• AI-native, provably so: You can show, concretely, how agent orchestration has changed your own output — examples, numbers, or a portfolio. Not just familiarity — results.
• High standards for output quality: AI-generated code is your code. You are not the manager who merges anything that compiles. You have a verification practice.
• Direct communicator: You give feedback clearly, make decisions without excessive consensus-building, and disagree with product or leadership when the technical reality demands it.
• High agency: You identify problems, propose solutions, and execute — you don't wait to be managed.

Nice to Have

• Experience in telehealth, DTC health, or a regulated healthcare environment
• You've built an internal agent framework or tooling layer that other engineers on your team used
• Shipped a consumer mobile app with measurable retention and a backend you owned
• Background in distributed systems or real-time infrastructure (WebRTC, event-driven architectures)
• You've written a post, given a talk, or built something in public about AI-augmented engineering

Why LumiMeds

AI as infrastructure, not a feature. We've already rewired how we build around AI. You won't be evangelizing something new — you'll be operating at the frontier with a team that's already bought in.

Real technical complexity. Clinical state machines, real-time patient-provider flows, high-stakes billing, HIPAA. The problems are hard because the domain is hard.

Small team, enormous leverage. You won't manage through layers. Your decisions show up in production the same week.

Direct impact. The systems you build affect patient outcomes. That's not a cliché here — it's the constraint that makes the work matter.