AI/ML Lead Engineer

About the role

Franklin Templeton is seeking an AI/ML Lead Engineer to design and implement agents for financial advisors that simplify 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.

Responsibilities

• 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
• Implement evaluation frameworks for multi-step reasoning accuracy, grounded-ness, hallucination mitigation, and financial correctness
• Optimize systems for latency, cost efficiency, and reliability in production
• Contribute to infrastructure decisions around model serving, vector databases, caching, and orchestration layers

Key Initiatives

• 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.

Requirements

• 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.

Qualifications

• 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.

Benefits

• Annual discretionary bonus
• 401(k) plan with a generous match
• Recognition rewards
• Comprehensive benefits package including health insurance, disability benefits, employee stock investment program, learning resources, career development programs, reimbursement for certain education expenses, paid time off, and a motivational wellbeing program

Pay

The annual salary for this position ranges between $180,000 – $212,000, depending on location and level of relevant experience, plus discretionary bonus.

Schedule

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.