Senior Analyst AI Implementation

1. Role Summary

Third Bridge produces primary research for private equity, credit, and corporate clients globally. The AI-PE team builds AI and data systems across the research lifecycle and across departments, from content production and client products to internal operations and coverage intelligence. We are building many pipelines and products at once, and we need people who can own them, not just build them.

This is a senior, hands-on role with two parts. First, you will own a portfolio of production systems and build new ones, taking problems from vague business requests to systems that teams rely on every day. Second, you will support multiple teams across the company, setting the engineering standard and growing two junior analysts into strong builders.

2. What You Will Own

You will be accountable for a portfolio of production pipelines and products, each with a clear owner, quality metrics, and an operating schedule. Across the portfolio, the work falls into four areas:

• Data and operations pipelines: scheduled, multi-source jobs that join data from internal and external systems, apply business rules, handle failures, and deliver results to analysts for review.
• LLM-based tools: summarisation, extraction, classification, and search across transcripts, research, and documents, built with retrieval, tool use, and agentic patterns where they fit.
• Client-facing AI products: AI research experiences delivered to institutional clients, with the reliability and compliance needed for external use.
• Coverage and demand intelligence: signals that show where research is thin, where demand is growing, and what to research next.

For each system you own, you will be responsible for:

• Scoping the problem with the business owner and agreeing on success metrics before building.
• Evaluation: a repeatable way to measure quality, catch regressions, and track cost and latency.
• Operations: monitoring, alerting, runbooks, and a clear path for handover when priorities change.
• Documentation that another engineer or analyst can use without you in the room.

3. Supporting Multiple Teams

The AI-PE team works with business and functional teams across the company, including content, sales and client services, product, technology, compliance, and data. You will:

• Act as the technical point of contact for the teams you support, translating fuzzy requests into concrete, shippable scope.
• Prioritise across teams, make trade-offs visible, and escalate conflicts to the VP early.
• Integrate builds into the wider stack with Technology and Data, and clear the path to deployment with Compliance.
• Choose the right tool for each job: a prompt, a retrieval step, an agent, a SQL query, or a rule. Explain the trade-off.
• Diagnose data-quality and performance issues across internal systems, and make pragmatic trade-offs when those systems impose constraints.
• Coach and review the work of junior analysts, so the teams output improves over time.

4. What You Bring

Must have

• 4+ years building and shipping software or data systems in production, including at least 2 years on LLM-based systems in production.
• Strong Python, with a track record of taking work from prototype to production: tests, monitoring, and iteration.
• Hands-on experience with LLM workflows: tool use or agentic patterns, retrieval over long documents, structured extraction, and evaluation you designed and ran yourself.
• Advanced SQL and a cloud data warehouse (BigQuery preferred), including scheduled jobs and data-quality checks.
• Experience matching records across messy sources, and knowing when deterministic rules beat a model.
• Experience owning several systems at once, prioritising between them, and keeping each one running after launch.
• Good judgement about where AI does and does not fit, and the ability to explain a trade-off to technical and non-technical partners.
• Experience reviewing other engineers work and mentoring people early in their careers.
• Comfort handling sensitive data and access controls, including identity and compliance requirements.

Nice to have

• Experience with the Claude API, MCP servers, or similar agent tooling.
• Experience reducing LLM cost and latency at scale.
• Experience working with business teams across several departments, such as sales, client services, or content.
• Consulting (MBB or Big 4) or high-growth startup experience alongside solid technical delivery.
• Interest in private markets, investment research, or expert networks.

5. What This Role Is Not

• Not a pure research role. The emphasis is on shipping and running reliable systems, not novel model development.
• Not an infrastructure ownership role. You will build in close partnership with the teams that own the underlying platforms.
• Not a people-management role at this level. You will mentor and lead by technical example.

6. How You Will Be Measured

• The reliability, quality, and business impact of the systems you own end to end.
• Portfolio health: uptime, data quality, and the share of systems with working evaluation and monitoring.
• Autonomy: how much can be handed to you with confidence, and how well you manage ambiguity.
• Effectiveness of your partnerships with the teams you support.
• The growth of the analysts you mentor, measured by what they can own independently after six months.

First six months

• Take ownership of an agreed set of existing pipelines and products, with documented owners, runbooks, and monitoring.
• Ship at least one new production system, with an evaluation harness and an agreed success metric.
• Set team engineering standards for code review, testing, documentation, and LLM cost tracking, and have them in use.
• Publish a prioritised roadmap across the teams you support, including data dependencies and risks.

7. Selection Process

• Conversation with the VP on the role, the team, and the work.
• Walkthrough of a production system you built or ran: failure modes, how you measured quality, and what you would change.
• Design exercise: how you would take on a new pipeline request from a business team, from scoping through to monitored production.
• Short take-home or code review of a junior engineers pull request, followed by a discussion.

8. Career Path

The natural progression is toward the AI Implementation Lead (AVP) track, subject to performance, sustained ownership of production systems across more than one team, growing cross-functional influence, and demonstrated coaching of analysts.

Additional Information 

What can you expect:

🌏 Work From Anywhere: Up to 4 weeks per year.

🏠 Hybrid Work: This role offers hybrid working model

📚 Yearly PDA Allowance: Dedicated annual budget to support your continuous growth.

🛡️ Health & Security: Private medical insurance, life insurance and 1to1help mental health support.

📆 Time Off: Upto 30 days of paid leave in a year

The successful candidate must, by the start of the employment, have permission to work in the country they are applying. 

We know that to be truly innovative, we need to have a diverse team around us. That is why Third Bridge is committed to creating an inclusive environment and is proud to be an equal opportunity employer. If you are not 100% sure if you are right for the role, please apply anyway and we will be happy to consider your application