Role Summary
The AI Implementation Lead operates much like a Forward Deployed Engineer within Third Bridge's Private Markets Strategy function. This is a hands-on, highly technical building role, embedded in Strategy rather than Technology. You will work closely with business problems across content operations, expert workflows, and client-facing AI products, translating them into working systems in partnership with our Technology, Product, Compliance, and Data teams. Over the first 12 to 18 months, you will also build and coach a small team of Applied AI Specialists.
Context
Third Bridge produces primary research for private equity, credit, and corporate clients globally. The Private Markets Strategy function owns how content is built, deployed, and scaled. We are integrating AI across every stage of the content lifecycle, from expert identification and vetting through transcript generation, Company Briefs, and demand-signal detection. This role is the strategic engine that turns these initiatives into working systems, in partnership with the technical teams that own the underlying infrastructure.
You will work across three broad areas:
- Internal content and operations systems: Tooling and data workflows to make content cleaner, faster to produce, and easier to discover.
- Client-facing AI products: The next generation of AI-powered research experiences, including agents built on leading voice and language model platforms.
- Demand and coverage intelligence: Systems that detect content gaps, client demand concentration, and prioritisation of companies, sectors, or themes, as well as systems to push relevant content to clients.
What You Will Do
Your focus will be on shipping outcomes, not managing tickets or writing decks. You will work in tight partnership with our Technology, Product, Compliance, and Data teams to move initiatives from concept into production. Core responsibilities include:
- Shape and drive AI initiatives: Take strategic AI priorities from concept through to production, designing, scoping, and delivering each build with engineering and product partners.
- Hands-on technical work: Write clean Python to prototype, wrangle, and integrate data from various internal sources (transcripts, CRM fields, portal activity) into environments ready for machine learning and LLM applications.
- Cross-functional partnership: Act as the primary strategic owner within Private Markets, coordinating closely with Technology, Product, Compliance, and Data to ensure initiatives are technically sound, commercially aligned, and compliant.
- Autonomous problem solving: Navigate ambiguous data environments, identify roadblocks, and engineer solutions to keep strategic projects on schedule.
- Content coverage and signal detection: Build tools and analyses to identify content gaps, client demand spikes, and prioritisation of companies, sectors, or themes.
- Expert and workflow enrichment: Contribute to AI-powered workflows for expert vetting and enrichment, collaborating with Product and Compliance on integration and guardrails.
- Team building: Recruit, onboard, and coach two Applied AI Specialists (Associate level) within the first 12 to 18 months.
Required Experience and Skills
- 6 to 9 years of combined experience across strategy and technical roles.
- Meaningful career experience in top-tier management consulting (MBB or Big 4) or a fast-moving startup, with demonstrable exposure to operating under ambiguity and shipping tangible outputs.
- Hands-on data science experience, with practical application to real business problems.
- Strong Python skills, extending beyond notebooks into repeatable, maintainable work.
- Practical experience applying LLM-based approaches, including retrieval-augmented generation, prompt engineering at scale, and basic evaluation.
- Comfort with messy, heterogeneous data from CRM systems, unstructured transcripts, and third-party sources.
- Strong commercial instincts, able to translate business problems into build plans for technical partners.
- Demonstrated ability to work autonomously with minimal oversight in a distributed team.
Nice to Have
- Prior exposure to expert networks, research, or primary information businesses.
- Experience with voice AI or conversational platforms.
- Experience building internal tooling for content operations, knowledge management, or enterprise search.
- Exposure to private equity or professional services client environments.