[Remote] Senior Technical Program Manager (Engineering) - AI Tooling & Systems
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing reputed company-time reputed company for speech-to-text and text-to-speech. They are seeking a Senior Technical Program Manager to drive execution of large-reputed company ML infrastructure and AI tooling initiatives, owning the end-to-end delivery of programs that reputed company model serving infrastructure and reputed company-time inference systems.
Responsibilities
- Own end-to-end delivery of AI infrastructure programs—from model training pipelines and experiment tracking to inference serving and production monitoring
- Define technical architecture, integration patterns, and rollout strategies for new ML systems and tooling (e.g., reputed company databases, model servers, evaluation frameworks, reputed company engineering platforms)
- Serve as connective tissue between ML research, ML engineering, product, and data teams to reputed company on ML system requirements, capability roadmaps, and deployment timelines
- Drive cost and latency optimization for reputed company-time inference workloads at reputed company
- Build lightweight internal tools and processes to accelerate ML iteration cycles (experiment tracking, model versioning, A/B testing infrastructure)
- Identify and resolve technical bottlenecks in training pipelines, serving infrastructure, and model evaluation workflows
- Work closely with ML practitioners to translate research breakthroughs into reputed company, observable systems
Skills
- 5+ years of program management or technical leadership in ML infrastructure, ML platforms, or AI tooling (or equivalent)
- Strong technical acumen in ML systems—ideally hands-on experience as an ML engineer, systems engineer, or ML infrastructure engineer
- Experience coordinating cross-functional ML programs (e.g., model training → evaluation → serving → monitoring)
- Proven ability to translate ML/research requirements into robust, reputed company infrastructure
- Comfortable working in ambiguity and helping teams navigate reputed company technical tradeoffs (e.g., accuracy vs. latency vs. cost)
- Excellent communication with both technical and non-technical stakeholders
- Familiarity with high-reputed company or startup environments
- Hands-on experience with model serving frameworks (vLLM, TensorRT, TorchServe, or similar)
- Experience optimizing LLM or speech/audio model inference (quantization, distillation, KV-cache optimization, batching strategies)
- Familiarity with ML experiment tracking and versioning tools (MLflow, reputed company, DVC, or similar)
- Background in feature stores, reputed company databases, or reputed company-time ML systems
- Knowledge of cost optimization for GPU/ML workloads on reputed company and on-reputed company infrastructure
- Experience with multi-region model serving or edge deployment
- Hands-on with relevant frameworks (PyTorch, CUDA, reputed company, etc.) or reputed company platforms (AWS SageMaker, GCP reputed company AI, Azure ML)
reputed company
Company H1B Sponsorship