AI Product Owner

Cyberhill Partners•Austin, TX

About The Position

As Product Owner, you will own the full product lifecycle of the runtime layer, from core inference engines and optimization tooling to orchestration, observability, and integration with enterprise AI factories. You'll work at the intersection of AI engineering, MLOps, and mission-critical deployment, ensuring our runtime delivers industry-leading speed, reliability, cost-efficiency, and compliance with frameworks such as NIST AI RMF.

Requirements

5+ years as a Product Owner, Product Manager, or technical lead in AI/ML platforms, with direct experience shipping production runtime, inference, or MLOps solutions
Strong understanding of AI runtimes (ONNX Runtime, TensorRT, TorchServe, vLLM, Triton), model optimization, containerization (Docker/Kubernetes), and orchestration tools
Proven track record delivering AI solutions to large organizations or USG customers; experience with NIST AI RMF, NIST CSF, or similar governance frameworks highly desired
Demonstrated ability to lead without direct authority and make trade-off decisions in ambiguous, fast-moving environments
Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, or equivalent experience
Ability to obtain and maintain a US security clearance preferred

Nice To Haves

Familiarity with causal inference tooling, digital twins, and synthetic data pipelines a strong plus
Hands-on experience with OWL ontologies, RDF, or semantic modeling for AI systems
Background in high-stakes domains such as aerospace & defense, manufacturing, or healthcare AI
Familiarity with Palantir ecosystem concepts or similar data/AI platforms
Experience building developer platforms or internal AI factories serving multiple downstream applications

Responsibilities

Define and prioritize the multi-year roadmap for the AI Platform, balancing scalability, performance, security, and advanced use cases like digital twins, causal inference, real-time simulation, and predictive maintenance
Partner with engineering, research, cybersecurity, and customer success teams to translate user needs, especially from Intelligence Community, DoD, and large enterprise customers, into actionable features
Own runtime features including model serving (ONNX, TensorRT, custom runtimes), quantization, distillation, dynamic batching, multi-framework support, edge deployment, and resource-efficient scheduling
Drive latency optimization, throughput scaling, fault tolerance, monitoring, and A/B testing for production AI workloads
Ensure runtime capabilities meet enterprise and USG security standards, including zero-trust architectures, explainability hooks, bias detection interfaces, and auditability
Run sprint planning, backlog grooming, and release cycles with engineering squads in an agile, OKR-driven environment
Track emerging runtime technologies, competitor capabilities, and customer demand in enterprise AI deployment
Support sales and solutions teams with technical demos, RFPs, master agreements, and proof-of-concept deployments