Lead Product Manager shaping AI - enabled appointment, queue, and video banking software for financial institutions. Driving product strategy, roadmaps, customer value, and responsible AI innovation.
About the role
- Principal Product Manager owning DataRobot’s Kubernetes orchestration platform for AI agents and models. Defining placement, scaling, lifecycle, isolation, APIs, governance, and reliability across enterprise deployments.
Responsibilities
- Own the deployment and workload API, including the resource model, lifecycle semantics, versioning, backward compatibility, and customer-facing error behavior
- Define placement and capacity across nodes and accelerators, including quota, priority, and contention handling across tenants
- Own autoscaling signals, cold-start and scale-to-zero economics, headroom policy, and cost-versus-latency trade-offs
- Own agent runtime decisions involving execution location, duration, isolation, tool calls, and state persistence through restarts or evictions
- Define ingress, routing, request-aware load balancing, tenancy boundaries, and private connectivity for model and agent endpoints
- Own governance and audit capabilities covering deployments, invocations, policies, access control, and audit durability
- Define metering, quota, tenant attribution, inference packaging, and pricing
- Own reliability SLOs, error budgets, and operational diagnostics for degraded deployments
- Hold the roadmap for the workload and agent runtime layer across two engineering pods
- Align with product teams building on top of the platform
- Operate through design reviews, API contracts, and production data
Requirements
- 6+ years in product management for infrastructure, developer platforms, or cloud services
- At least 3 years working on Kubernetes-based or distributed systems products
- Principal candidates bring 9+ years of experience and have owned a platform layer used by other product teams
- Deep technical understanding of GPU and accelerator behavior, including topology-aware placement, fractional and time-sliced sharing, MIG, device plugins, driver and container-runtime plumbing, memory constraints, and utilization economics
- Deep technical understanding of Kubernetes, including the API server and scheduler, controllers and CRDs, operators, admission and RBAC, device plugins, resource requests and limits, node pools, and pod scheduling failures
- Multi-tenancy experience, including isolation models, noisy neighbors, quota and fairness, and security-review-ready tenancy designs
- Experience owning a public or platform API
- Technical writing and prototyping ability, including documentation, deep dives, public posts, API references, or working prototypes
- Comfort operating with matrixed engineering teams and no direct reports
- BS or MS in Computer Science or a closely related technical field, or equivalent hands-on experience as a software, platform, or infrastructure engineer
- Nice to have: service networking depth, modern serving stacks such as vLLM, long-running and agentic workload patterns, customer-managed/air-gapped/sovereign deployments, regulated-industry experience, and CNCF or open-source contribution
- Ability to build prototypes, evaluation harnesses, agents connected to real services, or other working tools independently
Benefits
- Medical, Dental & Vision Insurance
- Flexible Time Off Program
- Paid Holidays
- Paid Parental Leave
- Global Employee Assistance Program (EAP)
- Competitive pay
- Reasonable accommodations for applicants with physical and mental disabilities
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