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

Calliere•New York, NY

4d•Onsite

About The Position

A rapidly scaling AI value-creation firm, operating at the intersection of consulting, venture building, private equity operations, and platform engineering, is seeking a senior Forward Deployed Engineer (FDE). This role involves embedding directly within portfolio companies to deploy production AI systems that significantly alter business operations. The firm utilizes a closed-loop model where hands-on engagements validate successful approaches, which are then spun into new ventures, inform acquisitions, and are compounded across a shared platform. This unique model offers a blend of consulting compensation, startup equity, venture equity, and participation in the firm's broader capital strategy.

Requirements

5+ years of experience shipping software or AI-enabled systems into production, with direct ownership of architecture decisions.
Experience building for external clients or business stakeholders, not solely internal engineering teams.
Strong communication skills, comfortable leading discovery conversations with non-technical leaders and translating between business and engineering.
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 problems, making tradeoffs, and standing behind decisions.
A track record of delivering tools that were adopted and used, not just prototypes.

Nice To Haves

Prior forward-deployed engineering experience at a well-known technical consultancy, or embedded/on-site technical roles within 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.

Responsibilities

Lead technical discovery directly with a business stakeholder, transforming vague pain points into scoped, buildable engineering problems.
Architect and build production-grade AI systems, including model/provider selection, retrieval and grounding strategy, guardrails, and graceful degradation/fallback paths.
Make critical build-vs-buy, custom-vs-platform, and no-code-vs-custom-code decisions for each engagement.
Own end-to-end reliability, encompassing error handling, retries, idempotency, monitoring, permissioning, and rollback strategies.
Review and unblock architectural decisions made by less senior engineers on the pod.
Present and defend technical tradeoffs directly to client leadership.
Translate built solutions into reusable patterns, templates, and evaluation frameworks for the broader platform team.
Partner with senior leadership on scoping and staffing upcoming engagements.