**About the project** ***(description, duration, stage)*** ---------------------------------------------------------- Join Neurons Lab as the **AI Architect** on a flagship engagement with a **European private investment group** — a holding company with a C\-level executive team, an investment/portfolio function and an affiliated family office. The programme builds **one private, access\-scoped context layer over the group's data** — calls, email, Slack and messengers, board protocols, decks, portfolio updates — and then **AI skills and agents that run on it**: first for the executive team, then for every employee. Two loops sit on the same layer: *alignment* (strategy, OKRs and goal drift made visible) and *efficiency* (a process miner that reads real workflows from the digital footprint, then optimizer agents that ship the automations). Four phases — **Capture Connect Distill Build** — over roughly **eight to ten two\-week sprints**, opening with a fixed\-fee two\-week **Sprint 0 readiness pass** (data\-access audit, ontology spec, legal checklist across jurisdictions). A family\-office workstream runs in parallel on the same squad. This is deliberately **not** a wrapper around an off\-the\-shelf platform. The client wants infrastructure they own, deployed privately, with role\-based access for people and full visibility for the AI. The same architecture becomes a **NeuronsLab product line**, so you are designing something that has to survive being redeployed for the next client. **Stage**: pre\-contract / design\-partner negotiation. **Duration**: multi\-phase, \~4–5 months to production for the executive pilot, with rollout beyond it. **Reporting**: CTO (@Alex Honchar) and CEO are in the room at every key point — architecture, sprint planning, sprint reviews. You own the technical decisions between those points, working alongside an AI Analyst (1\.0 FTE) and a Data Engineer (0\.5 FTE), plus the client's Head of Security from day one. **This role is full\-time.** **What you'll actually do** ***(example tasks)*** ------------------------------------------------- * **Run the Sprint 1 decision spike** and write the decision record: one central private\-cloud store vs. a semantic layer over the existing systems of record vs. ready platforms (Gemini Enterprise, Glean\-class, Cohere\-class, open components) — scored on security, access control, speed, cost and reversibility. * Design the **ontology / semantic layer** for the group: entities, relationships and business definitions spanning people, meetings, decisions, commitments, goals, deals, portfolio companies and documents. * Architect the **connector layer as an execution layer, not just an ingestion layer** — MCP / tool\-calling (Composio\-class or built) so agents can *act* in HubSpot, mail, Slack and internal systems, not merely read a stream of data. * Design **role\-scoped retrieval**: the principle is that AI sees everything and people keep role\-based access. Make that enforceable at the retrieval layer, not just in the UI, and evidence it to the client's security function. * Architect the **agent layer**: per\-executive skills (Chief of Staff / CIO / CFO / COO), the OKR \& drift coach delivered in Slack, and the **process miner optimizer** chain. * Choose and stand up the **private deployment** — VPC / on\-prem / managed, model selection and routing, cost and latency envelopes. * Build the **eval and observability harness**: correctness, groundedness, access\-boundary tests, regression suites before anything reaches an executive. * Establish **standards and failure\-mode design** — human\-in\-the\-loop boundaries for agents that take real actions, audit trails, rollback. * Stay hands\-on: implement the critical pieces yourself, review the pod's work, and keep the build **portable enough to redeploy** as a NeuronsLab offering. * Explain all of the above to a C\-level audience **in plain language**, in review sessions and working groups. **Skills** ---------- * **Agentic system architecture** end to end: retrieval, tools, orchestration, memory, evals, guardrails * **Ontology / knowledge\-graph engineering** and semantic layers over heterogeneous sources (RDF/OWL, Neo4j, dbt\-style modelling — pragmatism over purity) * **RAG / GraphRAG** at production quality, including hybrid retrieval and permission\-aware retrieval * **MCP, tool\-calling and connector platforms**; designing agents that perform actions with side effects safely * **Private / sovereign deployment**: VPC, on\-prem, self\-hosted or open\-weight models; AWS and/or GCP data \+ AI stack * **Identity, access control and data governance** applied to AI systems (RBAC/ABAC, scoping, audit) * Strong hands\-on **Python**; comfortable writing the hard 20% of the code yourself * **Evals \& observability** for LLM systems; treating quality as measurable, not anecdotal * Advanced written and spoken **English**; can hold an architecture conversation with a CIO and a CISO in the same meeting **Knowledge** ------------- * The current **enterprise context\-layer landscape** — Glean\-class platforms, Cohere\-class "AI OS" products, Microsoft Copilot / Agents, Gemini Enterprise, Palantir\-style foundries — and where each genuinely differs * **GDPR** and data\-residency constraints for multi\-jurisdiction European groups; what makes a private deployment defensible * **Financial services / private\-equity context** — investment policy, portfolio reporting, board process — a strong plus * OKR / goal\-management mechanics, enough to architect for them **Experience** -------------- * **7\+ years** hands\-on AI/ML engineering, of which **2\+ years building LLM / agentic systems in production** * **3\+ years** as technical lead or architect on client\-facing delivery * Demonstrated **ontology / knowledge\-graph or semantic\-layer** work over messy real\-world enterprise data * Experience with **regulated or security\-sensitive clients** (BFSI, government, healthcare) and private deployment * Experience in **consulting or a services business** — comfortable being the technical face to a C\-level client * Comfortable as the **most senior technical person on a 2\.5\-FTE pod**, with founders as sparring partners rather than a safety net
David Muñoz
Indeed · HR
David Muñoz
Indeed · HR
