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.

• Design and implement production-grade multi-agent systems using the 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 our client facing platform to surface insights in a natural manner for advisors

• Establish evaluation frameworks for multi-step reasoning accuracy, grounded-ness, 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

Advisor-Facing AI

• Design and implement agents for financial advisors that simplifies 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.

Workflow Automation

• 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.

AI Agent Platform & Infrastructure

• 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.

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.

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