AI/ML Lead Engineer

Franklin Templeton

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Stamford, CTSan Ramon, CASan Mateo, CA
Salary
$180,000–$212,000 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $204k
This role $196k
$138k $270k
below market most similar roles pay here above market

This role pays less than 53% of similar roles. Most pay $171,000–$237,640 — the blue band above. At the midpoint, this role pays about $196k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Franklin Templeton

Franklin Templeton is a global investment management organization offering a wide range of mutual funds, ETFs, and alternative investment solutions to retail and institutional investors worldwide. Industry: Investment Management & Asset Management

Franklin Templeton currently has 16 open roles on FindRole.

Listed pay typically runs $137,250–$174,500 across 16 roles with salary data.

Most-posted roles

View all roles at Franklin Templeton

At a glance

TL;DR · AI/ML Lead Engineer

The AI/ML Lead Engineer joins the AI platform team to design and implement production-grade multi-agent systems for financial advisors. This role involves building agent workflows that integrate context retrieval, reasoning, tool execution, and compliance checks to simplify advisor work and generate portfolio insights. You will develop distributed services with strong observability, establish evaluation frameworks for hallucination mitigation, and manage memory and state persistence. Key technologies include Python, LangChain, OpenAI function calling, and vector databases like Pinecone or FAISS. You will also work with RAG architectures, Docker, Kubernetes, and cloud platforms like AWS, Azure, or GCP. The work focuses on creating a scalable, auditable agentic platform to automate workflows and surface insights within a global asset management and custom indexing context.

What you'll do

  • Design and implement production-grade multi-agent systems using leading agent frameworks and platforms.
  • Build agent workflows that integrate context retrieval, reasoning, tool execution, and compliance checks.
  • Develop distributed services for agent execution with strong observability, monitoring, and failure handling.
  • Establish evaluation frameworks for multi-step reasoning accuracy, hallucination mitigation, and financial correctness.
  • Implement memory management, context handling, and agent state persistence strategies.
  • Embed AI agents and chatbots into client-facing platforms to surface portfolio insights for advisors.
  • Optimize systems for latency, cost efficiency, and reliability in production environments.
  • Contribute to infrastructure decisions regarding model serving, vector databases, and orchestration layers.

What we're looking for

  • Must have 5+ years of software engineering experience.
  • Must have 2+ years of experience building and deploying LLM, GenAI, or agent-based systems in production environments.
  • Must have experience implementing multi-step agent workflows using frameworks such as LangChain or OpenAI function/tool calling.
  • Must have expert-level proficiency in Python and experience building distributed services or microservices architectures.
  • Must have hands-on experience with vector databases, RAG architectures, and data grounding techniques.
  • Must have experience implementing observability, monitoring, and fault-tolerant systems for high-availability applications.
  • Must be authorized to work for any employer in the U.S.
  • Experience in financial services, AI evaluation, multi-agent systems at scale, or infrastructure and model serving (preferred).
  • Master's or PhD in Computer Science, Machine Learning, AI, or a related discipline (preferred).

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