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New York, New York, United States
Hybrid
WMG Technology’s Forward Deployed Engineering (FDE) team sits at the intersection of enterprise engineering and business enablement. We embed engineers with global business units — including Finance, HR, and our recorded music labels — to turn operational pain points into high-value AI and automation solutions.
Working directly with the business, we run a centralized intake, routing, and delivery model so the business can move in days or weeks using existing tools, while larger foundational work graduates cleanly onto the product roadmap. We set a shared technical bar, reuse what already works, and keep AI deployments secure, cost-transparent, and maintainable.
Your role:
Reporting to the Director of FDE & AI Optimization, the Engineering Manager will lead a team of embedded engineers and own how FDE work is taken in, built, reused, and graduated.
This is a hands-on people-manager role. You will coach engineers who sit inside lines of business while holding a centralized technical reporting structure, operating playbook, and delivery standard. You will partner with Data Engineer, Product Owner, and business sponsors to turn ambiguous requests into a clear, repeatable operating model — and to make sure FDE is a trusted path for AI optimization, not a shadow-IT queue.
The ideal manager brings an equal blend of technical judgment and organizational empathy: comfortable in the tools, rigorous about routing and reuse, and able to help non-technical stakeholders translate operational pain points into work the team can actually ship.
Build, coach, and manage a high-performing FDE team, including hiring, performance, and embedding engineers in specific lines of business while keeping them aligned to core tech standards.
Define, scale, and maintain the FDE technical operating playbook — intake standards, routing rules, reusable delivery models, and engagement lifecycle — so work enters, ships, and leaves FDE by clear criteria.
Turn ambiguous organizational processes (intake routing, priority rubrics, and graduation gateways) into clear, repeatable frameworks that business sponsors actually use.
Align engineers, product owners, business sponsors, and executive partners around shared technical standards, priority, and delivery roadmaps — and resolve boundary conflicts so there is one intake path, not shadow requests.
Champion reusable assets, developer tooling (including ADK/SDK patterns and an internal assets marketplace), and best practices so teams stop reinventing the wheel and production work has a named owner and a maintenance path.
Set the technical bar for bottom-up AI optimization: agents and automation on validated enterprise frameworks (including Databricks, Gemini Enterprise, Claude, and Airtable).
Own end-to-end operational governance for FDE deliveries — security and compliance, Cost of AI telemetry, and lifecycle management from prototype through production graduation or retire.
Partner with divisional leaders and budget owners to translate operational pain points into technical requirements, educate business units on how to work with tech, and give leadership a clear view of FDE capacity, outcomes, and aggregate AI cost.
Define success metrics for the FDE engagement model and run the operating rhythm (intake SLA, reuse, outcomes) that keeps the team efficient as demand scales.
You have 7+ years of experience in software engineering, solution architecture, data engineering, or AI/ML delivery, including 2+ years managing engineers.
You have led teams in a Forward Deployed Engineering, technical consulting, or heavily matrixed environment where talent is embedded in the business and still held to a central technical bar.
You have defined, scaled, and maintained operational playbooks — intake standards, routing, and reusable delivery models — not just written a process doc.
You can transform ambiguous processes (intake routing, priority rubrics, graduation gateways) into clear, repeatable frameworks that stick across teams.
You have championed reusable assets, developer tooling (e.g. ADK/SDK patterns), and best practices that improve team-wide efficiency and long-term asset maintenance.
You have owned operational governance across the delivery lifecycle, including compliance, cost telemetry (Cost of AI), and the path from prototype to production graduation.
You have deep familiarity with modern LLM ecosystems, agentic workflows, and cloud data platforms (e.g. Databricks, Gemini, Claude, Cursor).
You can evaluate business requests with discernment — distinguishing agile, low-effort optimization wins (days/weeks) from intensive initiatives that belong on a product roadmap (months/years).
You have exceptional written and verbal communication skills, with the ability to simplify complex AI paradigms into tangible operational value for non-technical stakeholders.
You have experience measuring engineering ROI and managing vendor/licensing or infrastructure cost in a production environment.
Experience rolling out an intake + routing model in a matrixed enterprise (what the team takes vs. routes to product, platform, or decline), with a visible backlog and an SLA on triage.
Hands-on delivery of agents, automation, or internal platforms on Databricks, Gemini Enterprise, Claude, Airtable, and/or Cursor.
Experience standing up a reuse path (starter kits, ADK/SDK, asset marketplace) and a written production-ownership / graduate-or-retire standard.
Music, media, or entertainment industry experience, or prior work supporting functions such as Finance, HR, or creative/label operations.
Comfort operating as a player-coach: close enough to the technical work to set standards, without needing to be the strongest coder on the team.
