Senior Director, Product Management – Managed AI & Inference
Crusoe
AWSAzureCloudGoGoogle Cloud Platform
Job Level
Mid-LevelSenior
Tech Stack
AWSAzureCloudGoogle Cloud PlatformGraphQL
About the role
- Take ownership of core Enterprise IT AI capabilities: connectors, LLM services, knowledge ingestion, guardrails, governance tooling.
- Partner closely with central AI Function, Infrastructure, End-User Support, Security, Legal, Privacy, and other IT teams.
- Define, deliver, and continuously improve technical products, integrations, and operating model enabling AI at scale.
- Own product vision, roadmap, and backlog; translate business needs into PRDs, epics, and acceptance criteria.
- Balance quality, cost, and latency trade-offs for AI workloads and align backend capabilities with agent experiences.
- Scope, design, and deliver secure AI connectors for enterprise platforms and partner on performance, resiliency, disaster recovery.
- Lead document classification, taxonomy initiatives, and define ingestion, enrichment, and retention standards for enterprise knowledge.
- Establish data access rules, redaction/DLP policies, and least-privilege models to protect PII and sensitive content.
- Define LLMOps quality metrics, run A/B experiments, and establish observability (feedback loops, drift detection, hallucination monitoring).
- Partner on policy, DPIAs, data residency, logging, audit, and define incident response runbooks; coordinate with Problem/Change Management.
- Establish RACI across AI, Enterprise IT, and business units; build enablement for End-User Support and coordinate training and adoption programs.
- Own OKRs and executive reporting for EIT AI; continuously re-prioritize based on business impact, risk, and feedback.
Requirements
- Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (Master’s is a plus).
- Minimum of 3 years delivering AI/ML or data-driven products in enterprise environments.
- Proven track record rolling out transformational capabilities at global scale (multi-region, multi-tenant, regulated environments).
- Hands-on familiarity with LLMs and AI platforms (OpenAI, Anthropic, Gemini, Azure OpenAI, AWS Bedrock); exposure to Google Agentspace preferred.
- Practical experience with RAG architectures, embeddings, prompt management, evaluation frameworks, and guardrails.
- Practical experience building secure connectors and APIs (REST/GraphQL), eventing, and middleware patterns.
- Experience with cloud platforms (GCP, Azure, AWS), identity and access (Azure AD/Entra, Okta), and secrets management.
- Experience with Enterprise IT platforms such as Microsoft 365/SharePoint, Jira/Confluence, Slack.
- Strong grounding in security, privacy, and compliance for AI (DLP, PII handling, retention, audit).
- Comfortable with Agile/Scrum; able to write crisp PRDs and sequence/architecture diagrams.
- Excellent communication and stakeholder skills, with the ability to influence, drive alignment, and lead change across technical and non-technical teams.
- Nice to have: ITIL v4 and cloud certifications (GCP, Azure, AWS).
- Corporate language English; submit application in English.
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