Lead, AI Implementation - MUM

The AI Implementation Lead sits within Third Bridge's Private Markets Strategy function and operates much like a Forward Deployed Engineer. Although the role is embedded in Strategy rather than Technology, it is a hands-on, highly technical building role. You will sit close to business problems across content operations, expert workflows, and client-facing AI products, and translate them into working systems in close partnership with our Technology, Product, Compliance, and Data teams. Over the first 12 to 18 months in role, you will also build and coach a small team of Applied AI Specialists reporting to you.

Third Bridge produces primary research for private equity, credit, and corporate clients globally. The Private Markets Strategy function owns how our content is built, deployed, and scaled for this client base. 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 those initiatives into working systems, in partnership with the technical teams that own the underlying infrastructure.

You will work across three broad surfaces:

1. Internal content and operations systems. Tooling and data workflows that make our content cleaner, faster to produce, and easier to discover across the business.
2. Client-facing AI products. The next generation of AI-powered research experiences that we deliver to institutional clients, including agents built on leading voice and language model platforms.
3. Demand and coverage intelligence. Systems that detect where our content is thin, where client demand is concentrating, and which companies, sectors, or themes we should prioritise next. They would also be responsible for developing systems to push relevant content to clients through the operational machinery.

Your weeks will be spent shipping outcomes, not managing tickets or writing decks. You will not own end-to-end technical deployment on your own; instead, you will work in tight partnership with our Technology, Product, Compliance, and Data teams to move initiatives from concept into production. Core responsibilities include:

1. →Shape and drive AI initiatives. Take strategic AI priorities from concept through to production, working alongside engineering and product partners to design, scope, and deliver each build.
2. →Hands-on technical work. Write clean Python to prototype, wrangle, and integrate data from messy internal sources, across transcripts, CRM fields, and portal activity, into environments ready for machine learning and LLM applications.
3. →Cross-functional partnership. Act as the primary strategic owner within Private Markets, coordinating closely with Technology, Product, Compliance, and Data to ensure every initiative is technically sound, commercially aligned, and compliant by design.
4. →Autonomous problem solving. Drop into ambiguous data environments, identify roadblocks, and engineer solutions that keep strategic projects on schedule.
5. →Content coverage and signal detection. Build tools and analyses that tell us where our content is thin, where client demand is spiking, and which companies, sectors, or themes we should prioritise next.
6. →Expert and workflow enrichment. Contribute to the AI-powered workflows that support expert vetting and enrichment, working with Product and Compliance on integration and guardrails.
7. →Team building. Recruit, onboard, and coach two Applied AI Specialists (Associate level) over the first 12 to 18 months in role.

To avoid ambiguity for candidates:

1. This is not a people-manager-first role. It is an Individual Contributor role with a small team growing underneath it. Most of your time should sit in building, shaping, and shipping.
2. This is not a PowerPoint or dashboard role. Strong stakeholder communication is expected, but the primary output is working systems.
3. This is not a pure research or data science role. You will use and integrate models, but the emphasis is on deployment, reliability, and integration rather than novel model development.

1. 6 to 9 years of combined experience across strategy and technical roles.
2. A meaningful portion of your career spent in top-tier management consulting (MBB or Big 4) or in a fast-moving startup environment, with demonstrable exposure to operating under ambiguity and shipping tangible outputs.
3. Hands-on data science experience, including practical application to real business problems rather than purely academic work.
4. Strong Python, including code that goes beyond notebooks into repeatable, maintainable work.
5. Practical experience applying LLM-based approaches, including retrieval-augmented generation, prompt engineering at scale, and basic evaluation.
6. Comfort with messy, heterogeneous data from CRM systems, unstructured transcripts, and third-party sources.
7. Strong commercial instincts. Able to sit with senior business stakeholders, absorb a business problem, and translate it into a build plan that technical partners can execute.
8. Demonstrated ability to work autonomously with minimal oversight in a distributed team.

1. Prior exposure to expert networks, research, or primary information businesses.
2. Experience with voice AI or conversational platforms.
3. Experience building internal tooling for content operations, knowledge management, or enterprise search.
4. Exposure to private equity or professional services client environments.

By the end of the first 12 months in role, we would expect:

1. At least three production-grade internal tools shipped (in partnership with Technology and Product) and used regularly by content or sales teams.
2. A clear coverage and signal view driving content prioritisation decisions across Private Markets.
3. The first Applied AI Specialist hired and productive.

Additional information