Lead – AI Optimization

Location: New York

At Franklin Templeton, we believe success is built through powerful partnerships. As a forward thinking asset manager, we build dynamic relationships with clients, understand their goals, and navigate complex markets together. We leverage cutting edge strategies and deep insights to unlock opportunities for long term wealth creation. Our talented, global teams bring expertise that is both broad and unique.

From our welcoming, inclusive, and supportive culture to our globally diverse business, we offer opportunities not only to help you reach your potential, but also to contribute to our clients’ success.

About the Department This role sits within Franklin Templeton's AI Platform team, which develops and supports shared AI tools and capabilities across investment, distribution, and corporate functions. The team partners with business stakeholders, product owners, engineers, and data and governance specialists to deliver practical AI solutions responsibly.

The team is hands-on and cross-functional, with a collaborative approach to solving business and technical problems. Team members are expected to challenge assumptions constructively, make sound engineering decisions, share knowledge, and take ownership of delivering solutions that are useful, reliable, and maintainable.

Job Overview The Lead - AI Optimization drives the technical delivery and continuous improvement of AI solutions across business groups. Working with engineers, data scientists, product owners, and business stakeholders, the role translates business needs into practical technical approaches and guides initiatives from discovery and solution design through implementation, testing, and production readiness.

Evaluation is a core responsibility: the role leads the development and improvement of reusable evaluation capabilities for large language models (LLMs) and AI applications. It establishes meaningful benchmarks, helps teams understand performance and limitations, and uses the findings to guide model selection and improvements in quality, reliability, latency, and cost.

This is a hands-on individual-contributor role with no people-management responsibilities. Leadership is exercised through technical ownership, direct contribution, mentoring, and influence. Clear communication is essential to aligning teams, explaining technical trade-offs, and keeping stakeholders informed of decisions, progress, and risks.

How you will add value

Lead technical delivery:
Translate business needs into practical designs, assessing feasibility, dependencies, and AI-assisted versus traditional approaches. Build critical components, review code, and guide initiatives through testing, deployment, and production readiness.

Own the evaluation framework:
Develop reusable tools for LLM and application evaluation, including benchmarks, representative datasets, scoring methods, and regression tests. Combine automated checks, LLM-as-judge methods, and human review to assess accuracy, grounding, safety, and reliability.

Optimize quality and performance:
Apply advanced prompting and improve retrieval, context construction, model selection, routing, and serving approaches. Use appropriate tuning, caching, and batching to improve quality and throughput while reducing latency and unit cost.

Validate improvements with evidence:
Run structured experiments and A/B tests with clear hypotheses, guardrails, and rollback criteria. Compare models, techniques, and vendors against internal benchmarks, document limitations, and use findings to prioritize changes.

Align teams and communicate clearly:
Partner across business and technical teams to resolve dependencies and meet enterprise standards. Explain trade-offs and evaluation results, document decisions, and…