Technical Product Manager – AI Platform
Location: Remote
Type: Contract
Duration: 6 months
Position Overview
Carex is partnering with Exact Sciences to identify an experienced Technical Product Manager – AI Platform to support the planning, coordination, and execution of a rapidly evolving AI/ML and agentic platform ecosystem. This role plays a critical part in translating AI platform strategy into actionable, dependency-aware roadmaps while ensuring reliable, safe, and cost-effective delivery of shared AI capabilities.
The Technical Product Manager brings strong product management rigor to highly technical platform work, balancing roadmap priorities, delivery risk, cost, and time-to-value. Acting as a steward of platform outcomes, this role ensures AI platform delivery aligns with enterprise priorities, customer needs, and measurable business impact.
Key Responsibilities
Platform Product Strategy & Roadmapping
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Translate AI platform strategy into a multi-quarter, dependency-aware roadmap aligned to business value, customer needs, technical risk, and strategic priorities.
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Apply product management best practices to platform and technical products, including prioritization, tradeoff analysis, and value-based decision making.
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Define platform value propositions, success metrics, and adoption goals in partnership with program and product leadership.
Execution & Delivery Leadership
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Scope, plan, and drive execution of AI/ML platform initiatives from kickoff through delivery.
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Own schedules, milestones, risks, and critical decision points across multiple squads and platforms.
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Identify, analyze, and actively manage cross-team dependencies that impact sequencing, value delivery, and risk.
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Lead prioritization decisions using value, cost, risk, and dependency analysis.
Backlog, Release & Risk Management
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Ensure high-quality backlogs with well-defined epics and initiatives tied to clear outcomes, success metrics, and customer value.
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Maintain consistent Definition of Ready and Definition of Done standards.
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Own platform release planning, including promotion criteria, rollout and rollback plans, and environment readiness.
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Maintain and actively manage a platform risk register with clear mitigation plans and accountable owners.
Governance, Reporting & Ways of Working
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Lead weekly product reviews focused on delivery status, blockers, and escalations.
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Publish regular product reporting, including delivery metrics, reliability indicators, and financial impact.
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Standardize ways of working, including Jira workflows, templates, checklists, and governance processes.
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Coach engineering and product partners on planning discipline, flow efficiency, and delivery predictability.
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Track capacity, resource utilization, and non-labor spend to support planning and budgeting.
Cross-Functional & Vendor Collaboration
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Partner closely with Engineering, Architecture, Security, Compliance, Data, and Product teams to ensure alignment with enterprise standards and policies.
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Manage vendor timelines, SOW deliverables, and integration checkpoints.
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Ensure product artifacts remain complete, current, and traceable from requirements through release.
Required Qualifications
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Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent professional experience.
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5–8+ years of experience as a Technical Product Manager or Product Lead supporting platform, infrastructure, or large-scale software systems.
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Demonstrated experience delivering products in cloud-native environments (e.g., AWS, Kubernetes/EKS, CI/CD pipelines).
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Strong understanding of AI/ML platform components, data and ML workflows, and/or agentic or LLM-based systems.
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Proficiency with Jira and Confluence and structured SDLC processes.
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Experience with release, change, risk, and dependency management.
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Strong analytical, communication, and stakeholder management skills.
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Authorization to work in the United States without sponsorship.
Preferred Qualifications
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Experience with agentic AI systems, LLM gateways, retrieval pipelines, guardrails, or evaluation frameworks.
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Experience delivering AI/ML platforms or distributed systems at enterprise scale.
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Exposure to regulated environments (e.g., HIPAA, audit trails, security reviews).
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Familiarity with observability and cost monitoring tools such as Grafana, Prometheus, or CloudWatch.
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Experience with ServiceNow or enterprise PMO tooling.
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