Sr. Forward Deployed Engineer — Portfolio Intelligence

About the role:
A leading global private equity firm is embedding a Senior Forward Deployed AI Engineer directly within its portfolio operations and technology function. This is a high-impact, high-visibility role — not a support function. You will move across the firm's portfolio of companies, identifying where AI can create measurable value, then building and shipping the production systems to deliver it. You own the problem and the solution.
Responsibilities:
Conduct structured discovery across portfolio companies and internal firm functions to identify high-value AI opportunities — bottlenecks, data silos, repetitive workflows, and decision latency
Own end-to-end solutioning: from opportunity framing and architecture design through to production deployment and ongoing performance monitoring
Build production-grade agentic AI applications, multi-step workflow automations, and intelligent integrations using Python, TypeScript, and modern AI frameworks
Design and implement data integration layers that connect fragmented portfolio company reporting, deal data, and market intelligence into unified, AI-queryable pipelines
Collaborate directly with deal teams, operating partners, portfolio company leadership, and firm technology teams — translating business problems into shipped AI solutions
Establish best practices for AI governance, model auditability, and responsible deployment within the context of financial services regulatory expectations
Build internal capability — documentation, tooling templates, and reusable components — that accelerates future AI deployment across the portfolio
What you bring:
10+ years in software or AI engineering with direct experience in private equity, private markets, investment banking, asset management, or financial services
Demonstrated production AI/ML deployment: LLMs, RAG pipelines, agentic systems, and AI workflow automation shipped and running in real environments — not sandboxes or POCs
Expert-level Python and strong TypeScript; proficiency with LLM orchestration frameworks — LangChain, LangGraph, LlamaIndex, or AutoGen — and prompt engineering at scale
Fluency in PE/private markets data and workflows — deal lifecycle, portfolio monitoring, LP reporting, fund accounting concepts, and the data that flows through each stage
Experience building AI integrations against financial data sources — Bloomberg, PitchBook, Preqin, data room platforms, ERP systems, or proprietary fund reporting tools
Cloud infrastructure competence — containerization, CI/CD for AI systems, and production observability; comfortable owning deployment without a dedicated DevOps team
Consultative mindset and executive presence — able to run a discovery workshop with a CFO or Operating Partner and walk out with a scoped AI brief
Nice to have
Prior forward deployed, implementation engineering, or embedded consulting role at an AI company or technology firm
Experience building against data room platforms — Intralinks, Datasite, Ansarada, or iDeals
Exposure to fund accounting systems — Allvue, Investran, Yardi, Juniper Square, or Chronograph
Familiarity with SEC, FINRA, or MiFID II compliance context for AI-generated outputs
Experience with structured and unstructured financial document parsing at scale
Background in operational due diligence, value creation planning, or portfolio operations

Work arrangement
Yes