AI Architect (UA/RU Language speaking)

About the project(description, duration, stage)

Join Neurons Lab as theAI Architect on a flagship engagement with aEuropean private investment group — a holding company with a C-level executive team, an investment/portfolio function and an affiliated family office.

The programme buildsone private, access-scoped context layer over the group's data — calls, email, Slack and messengers, board protocols, decks, portfolio updates — and thenAI 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) andefficiency (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 roughlyeight to ten two-week sprints, opening with a fixed-fee two-weekSprint 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 deliberatelynot 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 aNeuronsLab 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 theontology / semantic layer for the group: entities, relationships and business definitions spanning people, meetings, decisions, commitments, goals, deals, portfolio companies and documents.

• Architect theconnector layer as an execution layer, not just an ingestion layer — MCP / tool-calling (Composio-class or built) so agents canact in HubSpot, mail, Slack and internal systems, not merely read a stream of data.

• Designrole-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 theagent layer: per-executive skills (Chief of Staff / CIO / CFO / COO), the OKR & drift coach delivered in Slack, and theprocess miner → optimizer chain.

• Choose and stand up theprivate deployment — VPC / on-prem / managed, model selection and routing, cost and latency envelopes.

• Build theeval and observability harness: correctness, groundedness, access-boundary tests, regression suites before anything reaches an executive.

• Establishstandards 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 buildportable enough to redeploy as a NeuronsLab offering.

• Explain all of the above to a C-level audiencein 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-onPython; comfortable writing the hard 20% of the code yourself

• Evals & observability for LLM systems; treating quality as measurable, not anecdotal

• Advanced written and spokenEnglish; can hold an architecture conversation with a CIO and a CISO in the same meeting

Knowledge

• The currententerprise 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 which2+ years building LLM / agentic systems in production

• 3+ years as technical lead or architect on client-facing delivery

• Demonstratedontology / knowledge-graph or semantic-layer work over messy real-world enterprise data

• Experience withregulated or security-sensitive clients (BFSI, government, healthcare) and private deployment

• Experience inconsulting or a services business — comfortable being the technical face to a C-level client

• Comfortable as themost senior technical person on a 2.5-FTE pod, with founders as sparring partners rather than a safety net