Sr. Principal, Product Management

Smarsh • Atlanta, GA

1d

About The Position

Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6500 organizations in regulated industries counts on Smarsh every day to help them spot compliance, legal or reputational risks in 80+ communication channels before those risks become regulatory fines or headlines. Relentless innovation has fueled our journey to consistent leadership recognition from analysts like Gartner and Forrester, and our sustained, aggressive growth has landed Smarsh in the annual Inc. 5000 list of fastest-growing American companies since 2008.

Smarsh is the world’s leading provider of intelligence on sensitive communications for regulated industries. Global banks, insurers, and enterprises rely on Smarsh to detect risk, reduce noise, uncover insights, and apply compliant AI at scale. Our AI/ML platform integrates audio analytics, speech-to-text, OCR, translation, classification, and language identification to power supervision, surveillance, investigations, and next-generation GenAI-enabled workflows.
The AI Analytics capabilities and AI/ML models serve as core platform services used by every Smarsh product. Ensuring that the MLOps and Agentic infrastructure is robust and scalable while aligned with technology landscape is of strategic importance to the company.
Role Overview
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.

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.

Nice To Haves

• Experience with regulated industries or enterprise SaaS.
• Engineering or data science background
• Ability to engage credibly in architecture reviews — you do not need to write the code, but you need to understand the implications of the decisions being made
• Fluency in data residency concepts, multi-region cloud deployment patterns, and the engineering complexity they introduce for global AI products
• Understanding of model risk management (MRM) frameworks and how they apply to modern ML and LLM systems in financial services
• Comfort with ambiguity in emerging areas — Bring your own model strategy, agentic reliability, and multi-agent orchestration are fields where the playbook is still being written

Responsibilities

• 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 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
• 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
• 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.

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

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed