Our client is a PE firm with investment activities spanning private equity and other emerging-market strategies. Their current assets under management is US$1.6 billion.

The business is transforming itself into a digital and AI-driven platform. You will be one of the first two technology hires supporting the CTO & Chief Digital Officer in building that platform from the ground up.

Key Job Responsibilities:

• Build document intelligence. LLM-based extraction of structured data from the documents private markets run on: CIMs, financial statements, term sheets, legal agreements, DDQs.
• Build the knowledge layer. Retrieval-augmented search over the firm's deal history, research, and documents, so institutional knowledge stops living in inboxes.
• Automate workflows. Deal screening and market scanning, investor reporting, DDQ/RFP response drafting - anything repetitive that specialists currently do by hand.
• Own evaluation. Build the harnesses that measure whether AI outputs are accurate enough for an investment context. Precision matters enormously here, and "looks plausible" is not a bar.
• Drive adoption. Train the deal team on the tools you ship, gather feedback, run internal demos and lunch-and-learns, and iterate fast on what you learn.
• Prototype relentlessly. Evaluate new models, frameworks, and vendor tools; build quick proofs-of-concept; recommend build-vs-buy with evidence.

Key Job Requirement:

• A degree (2:1 or above, or equivalent evidence of ability) in computer science, engineering, maths, physics, or a similarly quantitative field or a demonstrable self-taught equivalent.
• Solid programming ability in Python (and ideally some TypeScript/JavaScript for lightweight UIs). You can show us things you've built.
• Hands-on curiosity about LLMs: you've built something with a model API (OpenAI, Anthropic, open-weights), even if it's a personal project.
• Product sense: you care whether people actually use what you build, and you'd rather ship something rough this week than something perfect next quarter.
• Clear communication. You'll demo to and train non-technical investment professionals.
• Care and discretion. You'll handle commercially sensitive financial data and must take security and confidentiality seriously.

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