Job Description

Applied AI Engineer (Full stack Forward Deployed Engineer)

Why Us / Why Now

We are the execution layer for enterprise AI transformation — a compounding value-creation engine across private equity and the enterprise. Four integrated pillars work as a closed loop: Transform (consulting proves value in the wild), Build (studio spins proven patterns into venture-backed companies), Buy (fund acquires where playbooks are proven), Accelerate (platform compounds every cycle). Execution fuels insight. Insight drives creation. Creation powers ownership

Traditional paths force trade-offs — consulting gives you cash but no ownership, startups give you equity but no stability, PE ops gives you capital but no builder culture. Our platform integrates all three – aligning long-term incentive with ownership. Cash covers your life. Platform equity compounds. Studio equity lets you participate in ventures you help create. Fund carry aligns you with capital strategy.

Role Overview

You are an Applied AI Engineer (FDE) — the forward-deployed operator inside a client pod, oriented to the build/deploy/adoption side of client work. You diagnose the business problem, design the solution on our s platform, and build the integration end-to-end: you're the problem-solver who also builds, combining technical skill with empathy for the client's actual workflows and business. You work embedded in client environments alongside a Senior FDE, Partners and Engagement Leads (who own the relationship and delivery), and AI Engineers (who extend the platform capabilities you depend on). At the mid-level tier, you're supported by a senior FDE on architectural calls and are responsible for owning workstreams within the engagement: shipping production-ready integrations, hardening systems, and contributing to the pod's adoption goals

What you'll own (outcomes)

• Production-ready integrations and automations that actually ship — not diagrams, working systems
• Workstreams within the engagement delivered on time and on quality, with acceptance criteria met before rollout
• Reliable production systems with measurable operational improvement (time saved, error reduction, throughput increase)
• Reusable patterns captured from your work for the platform and for your pod
• Clean handoffs: documentation, runbooks, and monitoring that enable maintenance without your direct involvement

What you'll do (responsibilities)

• Participate in technical discovery in client environments — map current-state workflows, understand existing systems, surface real constraints
• Build AI-enabled workflows: prompting, retrieval/grounding, model and provider selection with guardrails and fallbacks
• Prototype automations in days using no-code/low-code platforms (Relay, n8n, Zapier/Make, Lindy) combined with light Python/JavaScript
• Harden systems for production: error handling, retries, idempotency, logging, monitoring, permissions
• Implement against acceptance criteria, KPIs, monitoring, guardrails, and rollback plans defined for the engagement
• Handle secrets, PII, and compliance requirements aligned to client and our security standards
• Make and document explicit tradeoffs within your scope; escalate larger architectural calls to the Senior FDE
• Partner closely with AI Engineers on platform extensions and with the Senior FDE on integration design
• Interface with client working-team stakeholders to build confidence and align on approach
• Identify reusable patterns from your work and contribute them back to the platform

What we're looking for (requirements)

• 2–4 years of proven end-to-end delivery of software or workflow systems in production
• Solid solution and deployment architecture skills: APIs, integrations, data contracts, auth/permissions, reliability
• Fluency with no-code/low-code automation platforms (Relay, n8n, Zapier, Make, Lindy, or equivalent) AND ability to write Python or JavaScript
• Production reliability mindset: error handling, retries, idempotency, logging, monitoring, and permissions are part of every system
• Competence designing AI-enabled workflows: prompting, retrieval, model selection with guardrails and fallbacks
• Good judgment under time and ambiguity — you make tradeoffs within your scope, document them, and escalate when needed
• Clear technical writing: PRDs, interface contracts, runbooks, specs
• Problem-solving orientation with empathy — you surface real problems and design solutions people can use
• Comfort in client environments at the working-team level

Helpful if you have (preferred)

• Forward-deployed engineering experience (Palantir, consulting engineering, or client-embedded technical roles)
• Hands-on experience with LLM/agent architectures, orchestration patterns, and AI system design
• Background automating operations in consulting or professional services environments
• Experience with enterprise systems (CRM, ERP, ITSM, HRIS) via APIs and integrations
• Understanding of process mapping methodologies and operational design

Working Style

• You create clarity fast: define the problem, the scoreboard, and the next actions
• You default to shipping: concrete artifacts over meetings and opinions
• You do the unglamorous work to make systems reliable and measurable
• You’re direct and low-ego: you optimize for outcomes over credit
• You care about quality and user trust; you prevent regressions, not just fix them
• You’re curious and pragmatic about AI: push what’s possible, keep it grounded
• You build leverage: patterns, templates, and primitives that compound over time