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Open nowPosted 101 days ago

Senior Forward-Deployed AI Engineer (Build & Deploy Lead)

Calliere54 open roles

Where
New York, United States
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Your applicationOpen nowSenior Forward-Deployed AI Engineer (Build & Deploy Lead)Calliere · New York, United States

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Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 101 days ago

The posting

The Opportunity A rapidly scaling AI value-creation firm; one that sits at the intersection of consulting, venture building, private equity operations, and platform engineering is looking for a senior FDEr to embed directly inside portfolio companies and ship production AI systems that actually change how those businesses run. This isn't a typical consulting gig, and it isn't a typical startup role either. The firm operates a closed-loop model: hands-on engagements prove out what works, successful patterns get spun into new ventures, proven playbooks inform acquisitions, and a shared platform compounds the learning across every cycle. Instead of forcing you to choose between consulting cash, startup equity, or PE-style capital access, this model blends all three; a strong base, platform equity, studio equity in ventures you help spin up, and participation in the firm's broader capital strategy. What the Role Actually Is You'll be the senior-most technical presence embedded inside a portfolio company, working shoulder-to-shoulder with a business leader or subject-matter expert to figure out what's actually broken. Then, architecting and shipping the AI-powered system that fixes it. Not a demo. Not a slide deck. A tool the business runs on day to day. You'll own workstreams end-to-end, from the first discovery conversation through production deployment, while a Partner or Engagement Lead handles the broader client relationship around you. You'll also be the senior escalation point for the other engineers on your pod; mentoring them, unblocking hard architectural calls, and making sure nothing stalls on your watch. Core Responsibilities Lead technical discovery directly with a business stakeholder; turning vague, ambiguous pain points into a scoped, buildable engineering problem Architect and build production-grade AI systems: model/provider selection, retrieval and grounding strategy, guardrails, graceful degradation and fallback paths Make build-vs-buy, custom-vs-platform, and no-code-vs-custom-code calls for each engagement Own reliability end-to-end; error handling, retries, idempotency, monitoring, permissioning, rollback strategy Review and unblock architectural decisions made by less senior engineers on your pod Present and defend technical tradeoffs directly to client leadership Turn what you build into reusable patterns, templates, and evaluation frameworks for the broader platform team Partner with senior leadership on scoping and staffing upcoming engagements Requirements 5+ years shipping software or AI-enabled systems into production, with direct ownership of architecture decisions Experience building for external clients or business stakeholders, not just internal engineering teams Strong communicator; comfortable running discovery conversations with non-technical leaders and translating fluidly between business and engineering language Deep, hands-on fluency with AI-enabled workflows: prompting, retrieval/grounding, model and provider selection, guardrail and fallback design Solid software engineering fundamentals; APIs, integrations, data contracts, auth/permissions, production-scale reliability Comfort owning ambiguity: scoping the problem yourself, making the tradeoffs, and standing behind them A track record of tools that were actually adopted and used; not prototypes that quietly died Nice to have: Prior forward-deployed engineering experience at a well-known technical consultancy, or embedded/on-site technical roles inside client organizations Exposure to private equity operating environments, portfolio companies, or similarly fast-moving, high-stakes settings Experience mentoring or technically leading other engineers Familiarity with enterprise systems (CRM, ERP, ITSM, HRIS) and integrating AI tooling into them A background translating SME or business-leader domain knowledge directly into product requirements Benefits How Success Looks Here You build trust with business leaders as fast as you build the system itself. You turn messy, ambiguous business pain into something scoped and shippable without needing hand-holding. You default to shipping working tools over decks and diagrams. Reliability isn't optional to you. You're direct, low-ego, and know when to push back on a client's assumptions; and you mentor by unblocking people, not gatekeeping.

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