OT Artificial Intelligence Technical Product Owner (ACE AI Project)

OT Artificial Intelligence Technical Product Owner – Buenos Aires

OT Artificial Intelligence | OT Product Line

Position Overview

We are seeking an OT Artificial Intelligence Technical Product Owner to own the vision, roadmap and backlog for OT AI products delivered by a Buenos Aires-based product team. This role translates operational needs and prioritized use cases into a sequenced, deliverable product roadmap, ensuring AI capabilities move safely and consistently from intake and pilot into supported, measurable operational use.

The position is part of an OT Artificial Intelligence team within an OT Product Line and sets the day-to-day product direction for a cross-functional delivery team of data, machine learning, computer vision, MLOps and platform, edge integration, application development, and test and assurance engineers.

The role reports to the OT Product Line Manager and works within the architecture, standards and model lifecycle controls established by technical leadership and solution architecture. It enables OT AI priorities by converting approved use cases into reusable, secure and supportable products that business units can adopt without building isolated, one-off solutions.

The team is highly technical, creative and cross-functional. The Technical Product Owner is accountable for defining what the OT AI Product Team builds and in what order, balancing the needs of operations, engineering, data, cybersecurity, Responsible AI and business stakeholders.

Success means delivering measurable business value and adoption while maintaining safety, cybersecurity, reliability, human oversight, operator accountability and cost discipline.

High-Level Job Description / Scope

This is a dedicated OT AI product ownership role. It covers product vision, roadmap, backlog management, release planning, stakeholder engagement, adoption, value tracking and lifecycle management for OT AI products, including computer vision, time-series and predictive analytics, anomaly detection, generative AI and retrieval-augmented generation, agentic workflows, and AI-enabled applications deployed across edge, on-premises and approved cloud environments.

The role coordinates with existing OT delivery teams, including OT Data, OT Cloud and OT Engineering teams, and works within enterprise AI, data governance and cybersecurity frameworks to ensure OT AI products are safe, secure, reliable and supportable at scale.

Key Responsibilities

1. Own the OT AI product vision, strategy and roadmap for assigned products, aligned with organizational priorities and business unit needs.

2. Build, refine and prioritize the OT AI product backlog in Azure DevOps, writing clear epics, features and user stories with measurable acceptance criteria, nonfunctional requirements and definitions of done.

3. Partner with product managers, program managers and use-case sponsors to qualify intake, confirm fit with the product roadmap, avoid duplication and sequence new work against committed delivery.

4. Translate operational problems, value hypotheses and customer feedback into product requirements, success metrics and release plans the engineering team can execute.

5. Lead PI planning, sprint planning, backlog refinement, sprint reviews and demos; accept completed work and communicate progress, risks and dependencies to technical leadership and stakeholders.

6. Ensure products follow established AI reference architectures, guardrails, deployment standards and model lifecycle controls.

7. Guide products through pilot-to-production stages, including intake and qualification, design and pilot readiness, controlled pilot, production readiness and handoff, and ongoing operation, change or retirement.

8. Coordinate required reviews and approvals, including architecture, cybersecurity, applicable corporate policies, Responsible AI, data governance and site change management.

9. Define the intended autonomy level for each product (advisory, human-approved, bounded automation or autonomous) and ensure human oversight, fallback, rollback and operator accountability requirements are captured in the backlog.

10. Coordinate dependencies with existing OT delivery teams, platform owners, vendors and partner organizations so parallel efforts converge on shared, reusable capabilities.

11. Define product success measures and track value delivered, including adoption, production model performance, operational impact, support burden and cost.

12. Establish the operational handoff for each product, including named service and site owners, support runbooks, incident response, monitoring and retirement criteria.

13. Maintain product records in the AI product catalog and enterprise AI inventory, keeping documentation, decision logs and release notes current.

14. Provide product input to business planning and funding cycles, including effort estimates, licensing and consumption costs, business case inputs, and potential impacts on committed work.

Required Qualifications

1. Bachelor's degree in Engineering, Computer Science, Information Systems, Data Science or a related field, or equivalent experience.

2. Six or more years of experience delivering technology solutions in industrial, operational technology, data or AI environments, including at least three years as a Product Owner, Product Manager or Technical Lead.

3. Demonstrated experience owning a technical product backlog and roadmap within an Agile delivery environment such as Scrum or SAFe.

4. Experience writing epics, features and user stories with clear acceptance criteria and nonfunctional requirements using Azure DevOps or comparable tools.

5. Working understanding of AI and machine learning delivery, including data requirements, model training and evaluation, deployment, monitoring, drift and retraining.

6. Ability to discuss computer vision, time-series analytics, generative AI and AI-enabled application use cases in sufficient depth to guide engineers and challenge assumptions.

7. Understanding of OT environments and constraints, including safety, availability, network segmentation, constrained or disconnected connectivity and controlled change.

8. Working knowledge of OT cybersecurity concepts, including the Purdue reference model and ISA/IEC 62443 zones and conduits, and Responsible AI principles.

9. Experience leading delivery with distributed teams and stakeholders across countries and time zones.

10. Experience defining success metrics, tracking business value and making evidence-based prioritization and trade-off decisions.

11. Fluent written and spoken English, with the ability to communicate priorities, risks and decisions clearly to engineers, operators, cybersecurity reviewers and business sponsors.

Preferred Qualifications

1. Master's degree in Engineering, Computer Science, Data Science, Business Administration or a related field.

2. Product ownership certification such as Certified Scrum Product Owner, SAFe Product Owner/Product Manager or equivalent.

3. Experience owning AI, analytics or data products in oil and gas, energy, manufacturing or other asset-intensive industries.

4. Familiarity with Microsoft Azure AI and data services, Microsoft Foundry, Microsoft Fabric, Azure IoT Operations or Azure Arc-enabled edge platforms.

5. Experience with computer vision, robotics, drone or inspection use cases, including image data pipelines and model operations.

6. Familiarity with MLOps practices, model registries and production model monitoring.

7. Experience taking products through stage-gated governance, architecture review, cybersecurity concurrence or risk management processes.

8. Experience with industrial data sources and context such as historians, OPC UA, MQTT, asset hierarchies, knowledge graphs or digital twins.

9. Experience establishing support models, runbooks and operational handoff with managed service providers.

10. Spanish language proficiency and experience working with teams based in Argentina and the United States.

11. Experience operating within a stage-gated evaluation process and publishing a no-go or retirement decision as readily as a go decision.

Critical Selection Criteria

1. Product Ownership

Demonstrated ability to set product vision, own a prioritized backlog and deliver incremental value through Agile teams, using data and stakeholder input to make clear trade-off decisions.

2. Technical Capability

Credible understanding of the AI lifecycle and of OT edge, data, cybersecurity and operational layers, sufficient to guide engineers, apply architecture guardrails and challenge vendor claims.

3. Business Engagement

Ability to identify the operational problem behind an AI request, test whether AI is the right answer, and shape a product that operators, business owners and support teams will adopt and sustain.

4. Governance Discipline

Ability to plan for and complete required architecture, cybersecurity, applicable policy, Responsible AI and change-management reviews, and to move products through pilot-to-production gates without shortcuts.

5. Communication

Ability to explain product direction, priorities, AI limitations, risks and trade-offs clearly in writing and verbally to technical and nontechnical audiences across locations.