H
FDE - Applied AI Engineer
HRB
2 hours ago
Full-time
On-site
Applied AIEngineer(Full stack ForwardDeployedEngineer)
WhyUs / 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 (fundacquireswhere 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 noownership,startups give you equity but no stability, PE opsgivesyou 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 youparticipatein ventures you help create. Fund carry aligns you with capital strategy.
Role Overview You are an Applied AIEngineer(FDE) — theforward-deployedoperator 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 AIEngineers(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 andoursecurity standards
Make and document explicit tradeoffs within your scope;
Partner closely with AIEngineerson 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 anddeploymentarchitecture 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 elevate 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-deployedengineeringexperience (Palantir, consultingengineering, 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
#J-18808-Ljbffr
Traditional paths force trade-offs — consulting gives you cash but noownership,startups give you equity but no stability, PE opsgivesyou 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 youparticipatein ventures you help create. Fund carry aligns you with capital strategy.
Role Overview You are an Applied AIEngineer(FDE) — theforward-deployedoperator 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 AIEngineers(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 andoursecurity standards
Make and document explicit tradeoffs within your scope;
Partner closely with AIEngineerson 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 anddeploymentarchitecture 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 elevate 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-deployedengineeringexperience (Palantir, consultingengineering, 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
#J-18808-Ljbffr