AI/ML Lead Engineer

About the role

O'Shaughnessy Asset Management (OSAM) is part of Franklin Templeton, a forward-thinking asset manager that has built its success through powerful partnerships. 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. O'Shaughnessy Asset Management is a research and money management firm based in Stamford, Connecticut operating autonomously and backed with global, enterprise resources. Their approach to managing money is transparent, logical, and completely disciplined, leading to long‐standing relationships with clients. OSAM is a leading provider of Custom Indexing services via its Canvas® platform which offers financial advisors an unprecedented level of control and ease in creating and managing personalized separately managed accounts (SMAs) that target improved after-tax outcomes. For more firm information, please visit www.osam.com About the department 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. How you will add value 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 Key Initiatives this role will support 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. 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: Expe

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