Define and socialize the multi-year MLOps platform roadmap, spanning model training, evaluation, deployment, monitoring, and retirement across global regions
Architect product solutions for multi-region model inference with data residency constraints — ensuring our models operate compliantly across jurisdictions without sacrificing performance
Drive model observability and drift detection capabilities from concept to adoption, in partnership with Data Science and Engineering
Own the vendor and tooling strategy across the MLOps stack evaluating build vs. buy tradeoffs with a clear lens on total cost of ownership and compliance posture
Publish the Agent Framework Reference Architecture defining how autonomous and semi-autonomous agents are designed, tested, and deployed within a regulated FinServ environment
Lead product strategy for agentic workflow reliability, including human-in-the-loop design patterns, tool-use governance, and failure mode handling
Champion model governance as a product capability not a compliance checkbox by embedding risk controls directly into the platform so teams can move fast within guardrails
Drive adoption of the Model Governance Framework with Model Risk Management (MRM) teams, ensuring our AI systems meet regulatory expectations across all deployment regions
Define meaningful KPIs and observability standards that allow risk and compliance teams to assess model health without becoming a bottleneck to innovation
Develop and maintain a model risk taxonomy that scales across foundation models, fine-tuned models, and agentic systems
Partner with Applied ML, Engineering, Product, Finance, and Procurement to ensure alignment on strategy, performance, and cost.
Represent the MLOps and Agent Frameworks product domain to senior leadership, translating complex technical tradeoffs into strategic narratives that drive decision-making
Requirements
8+ years of product management experience, with at least 3 years focused on ML infrastructure, MLOps platforms, or AI/ML product strategy
Experience partnering directly with Data Science/Applied ML and Engineering teams.
Demonstrated experience shipping ML platform or AI infrastructure products in a regulated industry — financial services, healthcare, or similarly complex environments strongly preferred
Hands-on experience/ strong familiarity with MLOps tooling (e.g., MLflow, Kubeflow, SageMaker, Vertex AI, Databricks) and the ability to evaluate them critically as a product strategist
Working knowledge of LLM orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI) and the architectural tradeoffs they represent
Demonstrated experience managing build vs. buy decisions or large-scale vendor evaluations.
Excellent communication skills and ability to work across product, engineering, and commercial teams.
Willingness to support deal cycles with competitive insights and analytics expertise.
Benefits
Healthcare insurance
We provide medical, dental, and vision insurance, and a flexible spending account that allows you to set aside pre-tax dollars to pay for eligible out-of-pocket expenses.
Personal time off
A healthy work-life balance is critical to your success at the office. Smarsh offers a “take-what-you-need” time off policy as well as flexible work arrangements
401K Match
Smarsh provides a 4% 401K match for which employees are fully vested on day one.
Sabbatical – The Smarsh sabbatical program provides a time to recharge, to study or simply a time to do something you are passionate about away from the workplace. Employees are eligible after six years of service.
Recognition
We’re big on kudos for a job well done. Our employee-recognition program enables co-workers to nominate their peers who best embody our core values for recognition