AI/ML Lead Engineer

AI/ML Lead Engineer

Franklin Templeton is seeking an AI/ML Lead Engineer to design and implement agents for financial advisors that simplifies advisor work, leveraging client data and portfolio performance. Ideal candidates will generate insights for individual portfolios and across an advisor book of business, all within a monitored, auditable architecture. You'll be part of Franklin Templeton's AI platform team, where you'll help build the agentic platform and advisor-facing tools that are redefining how our advisors and clients engage with their portfolios. This is a chance to work at the intersection of cutting-edge AI and global asset management, owning foundational architecture and delivering capabilities that reach advisors and clients worldwide.

Key Initiatives this role will support:

• Advisor-Facing AI
• Workflow Automation
• AI Agent Platform & Infrastructure

What will help you be successful in this role:

• Required Skills (Must-Have)
• Production AI/LLM systems: 5+ years of software engineering experience, including 2+ years building and deploying LLM, GenAI, or agent-based systems in production environments.
• Agent frameworks and tool orchestration: Experience implementing multi-step agent workflows using frameworks such as LangChain, OpenAI function/tool calling, or similar orchestration frameworks.
• Programming and distributed systems: Expert-level proficiency in Python and experience building distributed services or microservices architectures.
• Data integration and retrieval: Hands-on experience with vector databases (e.g., Pinecone, FAISS), RAG architectures, and data grounding techniques.
• Production reliability and monitoring: Experience implementing observability, monitoring, and fault-tolerant systems for high-availability applications.
• Preferred Qualifications (Nice-to-Have)
• Financial services domain: Experience building technology solutions for asset management, wealth management, or portfolio analytics platforms.
• AI evaluation and model governance: Experience designing evaluation frameworks for LLMs (e.g., hallucination mitigation, groundedness, accuracy testing, or compliance monitoring).
• Multi-agent systems at scale: Experience designing or deploying multi-agent architectures involving memory, state management, and orchestration layers.
• Infrastructure and model serving: Experience with model serving frameworks, containerization (Docker/Kubernetes), and cloud platforms (AWS, Azure, GCP).
• Advanced degree: Master's or PhD in Computer Science, Machine Learning, AI, or a related discipline.

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time.

This is a hybrid role requiring individuals to work out of our Stamford, San Ramon, or San Mateo offices 3 days per week depending on the location of the candidate hired.

Franklin Templeton offers employees a competitive and valuable range of total rewards—monetary and non-monetary — designed to support their well-being and recognize their time, talents, and results. Along with base compensation, employees are eligible for an annual discretionary bonus, a 401(k) plan with a generous match, and recognition rewards. We also offer a comprehensive benefits package, which includes a range of competitive healthcare options, insurance, and disability benefits, employee stock investment program, learning resources, career development programs, reimbursement for certain education expenses, paid time off (vacation / holidays / sick / leave / parental & caregiving leave / bereavement / volunteering / floating holidays) and a motivational wellbeing program. We expect the annual salary for this position to range between $180,000 – $212,000, depending on location and level of relevant experience, plus discretionary bonus.

Franklin Templeton is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all applicants and employees, and we evaluate qualified applicants without regard to ancestry, age, color, disability, genetic information, gender, gender identity, or gender expression, marital status, medical condition, military or veteran status, national origin, race, religion, sex, sexual orientation, and any other basis protected by federal, state, or local law, ordinance, or regulation.