AI/ML Lead Engineer

Franklin Templeton • San Ramon, CA

3d • $180,000 - $212,000 • Hybrid

About The Position

Franklin Templeton is seeking an AI/ML Lead Engineer to join their AI platform team. This role will focus on designing and implementing agents for financial advisors that simplify their work by leveraging client data and portfolio performance. The ideal candidate will generate insights for individual portfolios and across an advisor's book of business within a monitored, auditable architecture. This is an opportunity to build the agentic platform and advisor-facing tools that are redefining how advisors and clients engage with their portfolios, working at the intersection of cutting-edge AI and global asset management. The role involves owning foundational architecture and delivering capabilities that reach advisors and clients worldwide.

Requirements

• 5+ years of software engineering experience, including 2+ years building and deploying LLM, GenAI, or agent-based systems in production environments.
• Experience implementing multi-step agent workflows using frameworks such as LangChain, OpenAI function/tool calling, or similar orchestration frameworks.
• Expert-level proficiency in Python and experience building distributed services or microservices architectures.
• Hands-on experience with vector databases (e.g., Pinecone, FAISS), RAG architectures, and data grounding techniques.
• Experience implementing observability, monitoring, and fault-tolerant systems for high-availability applications.
• 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.

Nice To Haves

• Experience building technology solutions for asset management, wealth management, or portfolio analytics platforms.
• Experience designing evaluation frameworks for LLMs (e.g., hallucination mitigation, groundedness, accuracy testing, or compliance monitoring).
• Experience designing or deploying multi-agent architectures involving memory, state management, and orchestration layers.
• Experience with model serving frameworks, containerization (Docker/Kubernetes), and cloud platforms (AWS, Azure, GCP).
• Master's or PhD in Computer Science, Machine Learning, AI, or a related discipline.

Responsibilities

• Design and implement production-grade multi-agent systems using leading agent frameworks and platforms.
• Build agent workflows that integrate context retrieval, reasoning, tool execution, validation, and compliance checks.
• Develop distributed services for agent execution with strong observability, monitoring, and failure handling.
• Establish tools, data agents, and services to enable context, ensuring the AI model is grounded in the correct data and knowledge.
• Embed AI agents and chatbots into the client-facing platform to surface insights naturally for advisors.
• Establish evaluation frameworks for multi-step reasoning accuracy, groundedness, hallucination mitigation, and financial correctness.
• Implement memory management, context handling, and agent state persistence strategies.
• Review interaction issues to continually refine knowledge bases and agent setups.
• Partner with product, design, and engineering teams to translate business requirements into robust agent architecture.
• Optimize systems for latency, cost efficiency, and reliability in production.
• Contribute to infrastructure decisions around model serving, vector databases, caching, and orchestration layers.
• Design and implement agents for financial advisors that simplify advisor work, leveraging client data, portfolio performance, thereby generating insights for individual portfolios as well as across an advisor book of business - all within a monitored, auditable architecture.
• Optimize client servicing, portfolio implementation, and other internal workflows using conversational and autonomous AI agents, this will include establishing a library of focused agents that are effective in their roles.
• Architect a scalable multi-agent platform with orchestration engines, memory and state management, dynamic tool invocation, structured output validation, observability, fault tolerance, and automated evaluation — solving reliability, explainability, and regulatory challenges at scale.

Benefits

• Competitive and valuable range of total rewards—monetary and non-monetary
• Base compensation
• Annual discretionary bonus
• 401(k) plan with a generous match
• Recognition rewards
• Comprehensive benefits package
• Range of competitive healthcare options
• Insurance
• 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)
• Motivational wellbeing program

What This Job Offers

• Job Type: Full-time
• Career Level: Senior