Technical Product Manager, Digital Solutions (Computer Vision / AI)

Patterson-UTI•Houston, TX

About The Position

Patterson-UTI’s Digital Solutions team builds real-time data streaming, visualization, drilling optimization, and data science products to improve safety and operational performance. The Technical Product Manager (TPM) for Digital Solutions will own the strategy and delivery of AI and computer vision enabled products that analyze real-time video from drilling environments to improve operational efficiency, safety (HSE), and decision-making. This role partners closely with software development, data science/ML, IT/OT, automation, operations, and HSE to define requirements, prioritize roadmaps, run pilots, and scale successful solutions across the field. The ideal candidate has experience developing and deploying computer vision(CV) systems (detection/segmentation/temporal analytics), managing data/labeling/evaluation processes, and shipping products on edge + cloud architectures. This role reports to the VP, Digital Solutions. Detailed Description: Influence the strategic vision for machine vision solutions and define an execution roadmap aligned to HSE priorities, operational KPIs, and automation initiatives. Serve as SME for functional specifications of AI-powered machine vision applications; enhance existing products and develop new concepts based on operational needs, customer requirements, and industry research. Own end-to-end product lifecycle from concept through launch and iteration, including planning, budgeting inputs, resource coordination, milestone tracking, and rollout readiness. Manage the product backlog using agile methods; partner with development teams to set sprint priorities and deliver against measurable outcomes. Establish validation processes for CV solutions, including manual and simulated lab testing prior to field deployment. Lead beta programs and field pilots, including rollout planning, stakeholder alignment, install coordination, data collection, and structured analysis. Define pilot acceptance criteria, measurement plans, and operational variable controls; translate results into recommendations for additional testing, iteration, scaling, or deprecation. Develop and maintain technical documentation (requirements, design specs, test procedures, training materials, technical presentations). Support launches and adoption through training and enablement for internal and external users; translate technical capabilities into clear value propositions (including marketing support as needed). Track industry trends in computer vision, AI, and edge computing; assess feasibility and impact, and recommend R&D investment opportunities. Technical Requirements: Guide design of high-bandwidth video ingestion and processing pipelines supporting RTSP/ONVIF feeds over industrial networks. Demonstrate working knowledge of video codecs and streaming constraints, including H.264/H.265. Partner with IT/OT to implement edge + cloud architectures that balance low-latency inference with cloud-based training, storage, and fleet management. Ensure data integrity, governance, and cybersecurity compliance for video assets transmitted from remote drilling locations. Guide model/application approach selection for real-world drilling conditions, including object detection, segmentation, tracking/temporal analytics, and pattern recognition, accounting for lighting variability, occlusion, motion, and site constraints. Own the ground-truth strategy and labeling workflow; oversee iterative evaluation using appropriate metrics (e.g., mAP, precision/recall, F1) and error analysis in field conditions. Drive repeatable, containerized edge deployment patterns (Docker; Kubernetes where appropriate), including versioning, rollout, and rollback expectations. Strong understanding of modern computer vision architectures/backbones (CNNs, vision transformers) and practical tradeoffs for edge inference.

Requirements

Operate independently and as part of a team while managing multiple workstreams and meeting deadlines.
Communicate clearly in writing and verbally; translate technical constraints into decisions, tradeoffs, and executive-ready risk assessments.
Travel up to ~30% with limited notice; occasional extended travel and overnight stays to remote locations.
Proficiency with MS Office (Word, Excel, PowerPoint) for presentations, reports, and schedules.
Safely access and navigate yard, shop, and drilling rig work sites, including day/night operations and outdoor hot/cold weather, while wearing required PPE.
Walk on uneven surfaces and perform regular walking, climbing, standing, stepping; maintain situational awareness around moving equipment and overhead machinery.
Lift and carry up to 50 pounds as needed during field visits.
Bend, stoop, kneel, twist, and crawl occasionally during field and operational area visits.
Remain alert during occasional extended work periods; listen for abnormal equipment or machinery noises.
Work weekends and provide after-hours on-call support as operational needs require.
Bachelor’s degree in engineering or related discipline.
8+ years of professional experience.
5+ years in technical product management delivering Industrial IoT (IIoT), edge computing, and AI-enabled applications.
Working knowledge of video codecs, networking protocols, and AI framework deployment (e.g., TensorFlow, PyTorch, TensorRT).
Experience in upstream Oil & Gas or comparable heavy industrial sectors (e.g., mining, maritime, heavy manufacturing).
Proven ability to influence cross-functional teams without direct authority and manage external technology partners.
Experience distilling complex technical constraints into clear risk assessments for executive leadership; experience delivering via Agile methodologies.
Strong understanding of modern computer vision architectures/backbones (CNNs, vision transformers) and practical tradeoffs for edge inference.

Responsibilities

Influence the strategic vision for machine vision solutions and define an execution roadmap aligned to HSE priorities, operational KPIs, and automation initiatives.
Serve as SME for functional specifications of AI-powered machine vision applications; enhance existing products and develop new concepts based on operational needs, customer requirements, and industry research.
Own end-to-end product lifecycle from concept through launch and iteration, including planning, budgeting inputs, resource coordination, milestone tracking, and rollout readiness.
Manage the product backlog using agile methods; partner with development teams to set sprint priorities and deliver against measurable outcomes.
Establish validation processes for CV solutions, including manual and simulated lab testing prior to field deployment.
Lead beta programs and field pilots, including rollout planning, stakeholder alignment, install coordination, data collection, and structured analysis.
Define pilot acceptance criteria, measurement plans, and operational variable controls; translate results into recommendations for additional testing, iteration, scaling, or deprecation.
Develop and maintain technical documentation (requirements, design specs, test procedures, training materials, technical presentations).
Support launches and adoption through training and enablement for internal and external users; translate technical capabilities into clear value propositions (including marketing support as needed).
Track industry trends in computer vision, AI, and edge computing; assess feasibility and impact, and recommend R&D investment opportunities.
Guide design of high-bandwidth video ingestion and processing pipelines supporting RTSP/ONVIF feeds over industrial networks.
Demonstrate working knowledge of video codecs and streaming constraints, including H.264/H.265.
Partner with IT/OT to implement edge + cloud architectures that balance low-latency inference with cloud-based training, storage, and fleet management.
Ensure data integrity, governance, and cybersecurity compliance for video assets transmitted from remote drilling locations.
Guide model/application approach selection for real-world drilling conditions, including object detection, segmentation, tracking/temporal analytics, and pattern recognition, accounting for lighting variability, occlusion, motion, and site constraints.
Own the ground-truth strategy and labeling workflow; oversee iterative evaluation using appropriate metrics (e.g., mAP, precision/recall, F1) and error analysis in field conditions.
Drive repeatable, containerized edge deployment patterns (Docker; Kubernetes where appropriate), including versioning, rollout, and rollback expectations.