Intermediate Client-Embedded AI Solutions Engineer (aka: FDE)

About the FirmWe operate at the intersection of AI consulting, venture building, and private equity; a firm designed so that operational work in client environments feeds directly into new venture creation and acquisition strategy, and vice versa. Rather than treating advisory work, startup building, and investment as separate businesses, we run them as one connected system: lessons learned deploying AI inside client organizations shape which companies we spin up, which existing playbooks we look to acquire, and how our internal platform evolves. The intent is to give people a mix most career paths don't offer: steady compensation alongside multiple forms of long-term equity upside (platform, venture, and fund-level).The RoleWe're hiring a mid-level engineer to work embedded inside client organizations as part of a small delivery team. You'll sit close to the actual business problem. Not just design a solution on paper, but build and ship the integration yourself. This role blends hands-on technical delivery with genuine attention to how the client's teams actually work day to day.You'll work alongside a more senior embedded engineer, an engagement lead who owns the client relationship, and platform engineers who build the underlying capabilities your integrations rely on. At this level, you'll get architectural guidance from a senior teammate but will independently own specific workstreams by shipping features, hardening systems for production, and helping the broader engagement hit its adoption targets.What Success Looks LikeIntegrations and automations that are actually running in production, not just proposedAssigned workstreams delivered on schedule and meeting agreed acceptance criteriaProduction systems with measurable operational impact — time saved, fewer errors, higher throughputReusable components or patterns from your work that others on the team can build onDocumentation, runbooks, and monitoring thorough enough that someone else could maintain what you builtCore ResponsibilitiesJoin discovery sessions inside client environments to map current workflows, understand existing tooling, and surface real constraintsDesign and build AI-driven workflows — prompt design, retrieval/grounding approaches, choosing models and providers, and putting guardrails and fallback logic in placeRapidly prototype automations using no-code/low-code tools alongside light custom scripting (Python or JavaScript)Bring prototypes to production-grade quality: error handling, retry logic, idempotency, logging, monitoring, and access controlBuild against clearly defined acceptance criteria, KPIs, monitoring plans, and rollback proceduresHandle sensitive data (secrets, PII) in line with client and internal security requirementsMake and document scoped technical tradeoffs, escalating bigger architectural decisions upwardCollaborate closely with platform engineers on extending shared tooling, and with your senior counterpart on integration designBuild trust directly with client working teamsFeed reusable patterns from client work back into the broader platformHow We WorkWe move fast toward clarity by defining the problem, the metric that matters, and the next concrete step. We'd rather ship something real than debate it in a meeting. We do the unglamorous reliability work most teams skip. We're direct, low-ego, and outcome-focused. We care more about preventing failures than firefighting them, and we stay curious about what AI can do while staying grounded about what it can't, yet.RequirementsWhat We're Looking For2–4 years shipping software or workflow automation systems that reached real production useSolid grasp of solution architecture: APIs, integrations, data contracts, auth/permissions, and reliability practicesComfortable with no-code/low-code automation tooling and able to write custom Python or JavaScript when neededA production-reliability mindset baked into how you build; not an afterthoughtPractical experience designing AI-enabled workflows: prompting, retrieval, model selection, guardrailsGood judgment under ambiguity and time pressure; you make calls within your scope and know when to escalateStrong technical writing; specs, interface contracts, runbooksA problem-solver's instinct paired with empathy for how the people using your systems actually workComfortable operating inside client organizations at the working-team levelNice to HaveExperience in a client-embedded or forward-deployed technical role (consulting-engineering backgrounds welcome)Hands-on work with LLM or agent-based system architecturesBackground automating operations inside consulting or professional-services firmsExperience integrating enterprise systems (CRM, ERP, ITSM, HRIS) via APIFamiliarity with process-mapping and operational design methods