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About This 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
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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
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- 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
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*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
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*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.
\#LI\-Hybrid
Franklin Templeton is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all applicants and employees, and we evaluate qualified applicants without regard to ancestry, age, color, disability, genetic information, gender, gender identity, or gender expression, marital status, medical condition, military or veteran status, national origin, race, religion, sex, sexual orientation, and any other basis protected by federal, state, or local law, ordinance, or regulation.
Salary Context
This $180K-$212K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Franklin Templeton, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($196K) sits 6% above the category median. Disclosed range: $180K to $212K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Franklin Templeton AI Hiring
Franklin Templeton has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Stamford, CT, US. Compensation range: $212K - $212K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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