Technical Program Manager

Job Title: <\/span>Technical Program Manager <\/b>
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The Technical Program Manager is not expected to code models, but must have sufficient technical depth to drive execution, manage trade‑offs, and unblock teams working on Linux‑based vision systems, sensors, PLC‑integrated environments, and real‑time data pipelines. <\/span>
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Key Responsibilities<\/b> <\/span><\/b>
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Program Ownership & Execution <\/span>
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Own end‑to‑end delivery of computer vision programs, from requirements definition through edge deployment and production rollout <\/span>
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Break down complex CV initiatives (model training, fine‑tuning, inference optimization, edge rollout) into clear milestones, timelines, and dependencies <\/span>
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Manage cross‑team dependencies across ML, embedded/edge, hardware, industrial systems, and UI/API teams <\/span>
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Technical Program Leadership (Computer Vision Focus<\/b>) <\/span>
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• Partner with Computer Vision Engineers building YOLO/CNN‑based models to align on execution plans, performance targets, and deployment readiness<\/span> <\/span>
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• Drive coordination across teams deploying models on Raspberry Pi, Jetson Nano, CPU/GPU edge platforms<\/span> <\/span>
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• Manage programs involving Linux systems, sensors, industrial cameras, PLC‑connected devices, and real‑time data streams<\/span> <\/span>
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• Ensure model training and tuning workflows using Amazon SageMaker are production‑ready and aligned to delivery timelines<\/span> <\/span>
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• Edge & Industrial Integration<\/span> <\/span>
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• Drive programs that integrate vision outputs into:<\/span> <\/span>
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• Dashboards and operational tools<\/span> <\/span>
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• APIs and backend platform services<\/span> <\/span>
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• UI and downstream consuming teams<\/span> <\/span>
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• Coordinate validation in industrial or field environments, managing constraints like latency, hardware limitations, and environmental variability<\/span> <\/span>
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• Risk, Metrics & Delivery Excellence<\/span> <\/span>
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• Identify risks related to:<\/span> <\/span>
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• Model accuracy vs. inference performance<\/span> <\/span>
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• Edge hardware constraints<\/span> <\/span>
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• Data quality, sensor reliability, and real‑time processing<\/span> <\/span>
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• Define and track program metrics such as model readiness, deployment success rates, latency targets, and operational stability<\/span> <\/span>
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• Escalate issues early and drive data‑based trade‑off decisions<\/span> <\/span>
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• Communication & Stakeholder Management<\/span> <\/span>
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• Communicate program status, risks, and decisions clearly to senior technical and business stakeholders<\/span> <\/span>
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• Serve as the single‑threaded owner for Computer Vision programs across multiple teams<\/span> <\/span>
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• Translate engineer‑level detail into executive‑level updates<\/span> <\/span>
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  Basic Qualifications <\/b>
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  3–5+ years of experience in technical program management or systems program management <\/span>
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  Experience working with computer vision, ML systems, edge computing, or embedded systems teams <\/span>
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  Strong understanding of:<\/b> <\/span><\/b>
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  • Linux environments<\/span> <\/span>
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  • Camera/sensor‑based systems<\/span> <\/span>
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  • Model training vs. inference trade‑offs<\/span> <\/span>
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  • Demonstrated ability to manage cross‑functional technical programs involving software, hardware, and data pipelines<\/span> <\/span>
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  • Strong written and verbal communication skills<\/span> <\/span>
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    Preferred Qualifications<\/b> <\/span><\/b>
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    • Experience with edge AI deployments (Jetson, embedded GPUs, industrial edge devices)<\/span> <\/span>
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    • Familiarity with Amazon SageMaker workflows for model training and tuning<\/span> <\/span>
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    • Exposure to industrial systems, PLC‑integrated environments, or real‑time streaming architectures<\/span> <\/span>
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    • Experience delivering systems that integrate ML outputs into APIs, dashboards, or operational UIs<\/span> <\/span>
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