Technical Program Manager, Platform
Leads end-to-end delivery of generative AI platform and infrastructure initiatives, coordinating engineering, ML, research, product, and executive stakeholders. Requires 5+ years of technical product or platform experience, including infrastructure program management and familiarity with cloud-native, distributed, and AI/ML systems.
About the job
Responsibilities
- Lead strategic planning and high-velocity execution for core platform capabilities, including orchestration layers, model serving, and APIs, from technical scoping and architecture design through production launch.
- Drive execution and manage technical dependencies across systems engineering, Core ML, Research, and Product teams.
- Translate infrastructure metrics such as LLM inference optimization, GPU utilization, and compute orchestration into actionable roadmaps.
- Map requirements including multi-tenancy, data privacy, and isolation into platform features.
- Identify, track, and mitigate technical risks associated with large-scale generative AI infrastructure and global platform deployments.
- Establish agile processes that enable rapid, reliable engineering delivery.
- Define and enforce SLOs and performance benchmarks for production reliability.
- Track and report platform adoption, system reliability, delivery forecasts, and engineering bottlenecks to executive leadership.
Requirements
- 5+ years of experience as a Technical Program Manager, Product Manager, or Software Engineer, with a track record of building and shipping technical products or platforms from scratch.
- 3+ years of experience managing programs focused on core engineering infrastructure, cloud-native ecosystems, container orchestration, or distributed systems.
- Foundational understanding of generative AI infrastructure, including high-throughput data pipelines, GPU/CPU cluster utilization, and model training or evaluation environments.
- Experience presenting to and influencing executive-level stakeholders.
- Ability to translate complex technical and architectural challenges into clear business impacts.
- Advanced proficiency with iterative development methodologies and project management tools.
Nice-to-Haves
- Prior professional experience as a Software Engineer, DevOps Engineer, or Data Developer.
- Experience driving internal adoption of technical platforms, SDKs, or APIs across product lines.
- Experience with large-scale data quality pipelines, distributed vector databases, or AI inference engines such as Triton or Ray.
Compensation and Benefits
- No compensation information provided.
Skills
Generative AI, Distributed Systems, AWS, GCP, Kubernetes, Llm Inference, Gpu Computing, Data Pipelines, Model Serving, APIs, SLOs, Jira, Linear, Triton, Ray
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