Technical Program Manager

About US

GMI Cloud is a fast-growing, AI-native infrastructure company delivering high-performance GPU compute, inference services, and infrastructure for AI agents. Following 8x ARR growth, GMI Cloud continues to scale rapidly across the U.S. and APAC. As a Reference Platform NVIDIA Cloud Partner (NCP) and a validated leading NCP across both markets, we power production AI for leading AI-native companies including Fireworks AI, Cartesia, Reflection, and OpenRouter. From large-scale compute to optimized inference and agentic workloads, GMI Cloud gives AI teams the infrastructure they need to build, deploy, and scale on one unified cloud. One cloud for compute, inference, and agents.

About this role

We are looking for a
Technical Program Manager (TPM)
who combines strong
program ownership, production sense, and execution rigor
with a solid
technical foundation in AI infrastructure and distributed systems
.

In this role, you will
own and drive complex, cross-functional programs
that span GPU infrastructure, Kubernetes platforms, inference/training systems, and customer-facing AI services. You will ensure that high-impact initiatives move from
design → implementation → production → scale
, on time and with high quality.

This is a hands-on TPM role for someone who can:

• Speak fluently with engineers
• Think like a product manager about tradeoffs and user impact
• And execute like an owner in a fast-moving environment.

Key Responsibilities

Program Ownership & Delivery Excellence

• Own end-to-end delivery of complex technical programs across AI infrastructure, GPU platforms, and cloud services.
• Define clear goals, milestones, dependencies, risks, and success metrics for multi-team initiatives.
• Drive execution rigor: timelines, accountability, escalation, and delivery predictability.
• Ensure programs reach
  production-ready quality
  , not just prototype or design completion.

Technical & Production Sense

• Partner closely with engineering to translate technical designs into executable delivery plans.
• Apply strong production judgment around reliability, scalability, performance, and operational readiness.
• Identify risks early (capacity, performance, security, operational complexity) and drive mitigation plans.
• Ensure launches include proper monitoring, alerting, rollback strategies, and operational ownership.

Cross-Functional Leadership

• Act as the connective tissue across Engineering, Product, Infrastructure, SRE, and Go-To-Market teams.
• Align stakeholders on priorities, tradeoffs, and sequencing in a resource-constrained environment.
• Communicate clearly and concisely to both technical and non-technical audiences, including leadership updates.

Platform & Infrastructure Programs

• Drive programs related to:
• GPU cluster expansion and lifecycle management
• Kubernetes platform evolution
• AI inference and training infrastructure
• Internal developer platforms and tooling
• Coordinate roadmap execution across multiple regions and environments.

Minimum Qualifications

• Program Management:
  Proven ability to drive cross-functional technical programs from design to production with strong ownership and execution discipline.
• Production Sense:
  Strong judgment around API reliability, latency, scalability, rollout quality, and operational readiness for production AI services.
• AI / LLM Systems:
  Solid understanding of LLM and multimodal model inference workflows, including text, image, audio, or video APIs.
• Inference & Serving:
  Familiarity with model serving concepts such as throughput, tail latency, batching, streaming, and cost-performance tradeoffs.
• AI Infrastructure:
  General understanding of GPU-based inference systems and their impact on performance and scalability.
• Communication:
  Clear, structured communication with engineering, product, and leadership stakeholders.

Preferred Qualifications

• Experience managing technical programs related to AI products, model APIs, or inference platforms.
• Hands-on exposure to LLM or multimodal inference systems in production environments.
• Familiarity with AI model APIs, SDKs, or developer-facing AI platforms.
• Strong technical foundation with the ability to reason about system tradeoffs and customer impact.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.