Senior Principal Product Management, Smarsh

• You combine technical depth with strong commercial judgment, and thrive in a role that demands rigor, clarity, and cross-functional influence. You understand the AI/ML ecosystem
• You are highly collaborative and able to work across multiple product teams, engineering, Applied ML, and go-to-market stakeholders. You bring customers’ needs and competitive pressure into your product thinking, and you excel in environments where build vs. buy decisions shape long-term strategy
• 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
• Don't meet every requirement? Apply anyway! We value diverse candidates and encourage applications, even if you don't perfectly match the job description. Studies have shown that some strong candidates may self-select out of the interview process prematurely, at Smarsh we encourage an inclusive, high-performing environment

Desirable

• We are seeking a product leader to own MLOps and Agentic strategy for Smarsh’s Analytics Platform
• Models are proliferating faster than the infrastructure to govern them, and agentic workflows are moving from proof-of-concept to production with real money, real customers, and real regulatory scrutiny on the line
• This is not a role for someone who thinks about MLOps and agent frameworks separately
• We need a product leader who sees them as two sides of the same coin: the platform that governs how models are built, deployed, and monitored, and the orchestration layer that determines how those models act autonomously on behalf of our business and customers
• As an Individual Contributor Director, you will operate with the seniority and strategic authority of a Director while remaining deeply embedded in the work of shaping roadmaps, influencing architecture, and driving adoption across engineering, risk, compliance, and business stakeholders
• You will report directly to the VP of Product and own one of the most consequential product domains in our AI strategy
• MLOps Platform Strategy
• 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
• Agent Frameworks & Orchestration
• Publish the Agent Framework Reference Architecture efining 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
• Define and document the Bring Your Own Model establishing how the organization governs the introduction of external models and custom prompts while maintaining auditability and control
• Translate emerging agent framework patterns (e.g., multi-agent orchestration, RAG pipelines, memory and context management) into concrete product requirements and phased delivery plans
• Model Governance & Risk
• 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
• Stakeholder Influence & Alignment
• 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
• Establish and govern the product council or working group that coordinates AI platform decisions across business units and geographies
• Collaborate with product teams to embed analytics consistently into their workflows