Founding AI Platform Engineer (Agents & RAG) - Fulltime - EMEA/LATAM/NA

About the firm

A newly established multi-strategy investment and advisory firm that acquires small and mid-sized businesses and runs an advisory practice, with a presence in Dubai and New York. The firm is building a proprietary, AI-enabled operating platform used across its advisory work, its own investment operations and, later, its portfolio companies. Firm name available to shortlisted candidates.

Role overview

The first engineering hire. You build and run the agent platform behind the firm's advisory services, then extend it to investment operations and portfolio companies. The role works across all business lines and grows into the firm's first forward-deployed engineer.

Key responsibilities

• Interim setup: Stand up one enterprise AI platform on a private cloud account; structure all work as missions with approval checklists, an evidence log and per-client folders.
• Platform MVP: Build the first agents (mission manager, intake & data, analyst, evidence auditor), the evidence ledger and the approval console. First use case: an AI operations diagnostic for advisory clients.
• Full platform (v1): Scale to a roster of ~12 agents under a manager agent; build a model gateway that selects the best model per agent, with review agents on a different model family; RAG from day one; tiered data layers (firm-wide, advisory, per-client encrypted vaults); hard-stop approval gates.
• Client delivery: Run agent work in the firm's sandbox on copies of client data; support human-led deployment into client systems under time-limited, logged access; build read-only KPI feeds.
• Investment automations: CIM analysis; deal pipeline management and outreach; three-statement analysis, DCF/LBO model building and investment memo drafting for screened deals.
• Expansion: Keep the platform model-agnostic and take it from advisory to firm operations to portfolio companies.

Non-negotiable standards

• Security and containment for client-system connections and financial data.
• Information barriers enforced in code between business lines and data tiers.
• Agents never receive write access to client systems; human review at every milestone.
• Frontier models accessed through the gateway; no in-house model training.

Required qualifications

• Hands-on experience building LLM agent systems: orchestration, tool use, multi-agent workflows.
• Retrieval-augmented generation (RAG) and data-pipeline design in production.
• Cloud security and identity & access management (IAM); audit logging and observability.
• Integrations via APIs and MCP; experience with the Microsoft enterprise stack.
• Ability to work independently as the sole engineer and own systems end to end.

Preferred qualifications

• Working knowledge of finance: CIMs, three-statement models, DCF/LBO, investment memos.
• Experience in regulated or confidential-data environments (financial services, advisory).
• Client-facing or forward-deployed engineering experience.