Sr. Principal, Product Management
AtlantaFull-TimeStaffProduct / Project
How will you contribute?
- 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.
What will you bring?
- About You
- 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.
- Required
- 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.
- Strong Pluses
- 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
What do we offer?
- 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
