Senior Applied AI Engineer – Global Investment Boutique – 500k+

A global boutique hedge fund is making its first dedicated applied AI hire and looking for an engineer to build the firm’s AI capability from zero. LLMs, RAG, agentic tooling, production ML. You define the stack, the strategy, and how a sophisticated firm actually adopts AI.

This is a foundational, high-visibility role with unusually broad scope. You’ll identify, build, and ship AI-driven solutions across the business (investment, operational, and corporate functions) while shaping the firm’s AI strategy, tooling, and infrastructure from the ground up. The work rewards someone who pairs real hands-on engineering with the judgment and communication to win organizational buy-in and prove value fast.

What You’ll Do

• Design, build, and deploy applied AI and LLM-based solutions that deliver measurable value across the firm.
• Establish the firm’s AI stack end to end: tooling, infrastructure, frameworks, and best practices.
• Partner with stakeholders across the business to find high-impact use cases and turn them into production systems.
• Drive adoption of AI across technical and non-technical teams, building awareness and trust in what the capability can do.
• Own initiatives end to end, from experimentation and prototyping through production deployment and iteration.

Must-haves:

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, or a related field, or equivalent professional experience.
• Strong software engineering skills with hands-on experience building and shipping applied AI, ML, or LLM systems in production.
• Deep familiarity with the modern AI stack: LLM frameworks, orchestration tooling, vector databases, retrieval-augmented generation (RAG), and related infrastructure.
• A track record of driving initiatives from concept to delivery with minimal direction.
• Strong communication and stakeholder management, with the ability to build adoption across an organization.
• An entrepreneurial mindset and comfort operating as the founding member of a new capability.

Nice-to-haves:

• Experience with agentic systems, fine-tuning, or evaluation frameworks.
• Exposure to a hedge fund, trading, or other data-rich, high-stakes environment.
• MLOps / LLMOps experience taking models from prototype to reliable production