Product Owner

Tactacam•Billings, MT

About The Position

Tactacam is seeking a Product Owner for AI Platforms & Vision Systems to lead the development of our AI capabilities through strategic third-party partnerships and drive their integration into internal systems and customer-facing products. This role sits at the intersection of AI strategy, computer vision, large language models, and product execution — focused on building scalable AI-powered capabilities across image/video understanding, intelligent automation, and data-driven user experiences. You will own the AI platform roadmap, working closely with external AI partners to evaluate and refine model capabilities, while defining how those capabilities are integrated across applications, services, and devices. This includes advancing computer vision use cases as well as broader AI applications that improve product functionality, customer interactions, and internal operations. This is a builder role for someone who is equal parts Product Owner, technical partner manager, and integration architect. The ideal candidate has shipped AI, computer vision, or LLM-powered products, is comfortable managing technical vendor relationships, and can translate model capabilities into real-world product features and operational improvements.

Requirements

Bachelor’s degree in computer science, engineering, data science, or a related field (preferred but not required).
3–5 years of Product Owner (or similar product role) experience, including direct ownership of the roadmap, backlog, and delivery for technical products.
At least 1 year of hands-on experience working on AI/ML-powered products, including computer vision and/or large language model use cases.
Demonstrated experience shipping AI-driven features from concept to production, including defining requirements, working with engineering teams, and measuring outcomes post-launch.
Practical experience working with large language models in a product setting, such as prompt design, retrieval-augmented generation (RAG), tool/function calling, or evaluation of model outputs.
Experience working with or evaluating third-party AI vendors or foundation model providers, including comparing capabilities, performance, and cost trade-offs.
Solid understanding of core AI/ML concepts, including the differences between training, fine-tuning, and prompting, as well as common evaluation metrics (e.g., precision/recall, accuracy, latency, cost-per-inference).
Experience defining API contracts and integrating external services into applications, including handling reliability, fallbacks, and edge cases.
Familiarity with model evaluation workflows, including use of labeled datasets, test cases, and structured evaluation approaches for both vision and language models.
Strong written communication skills, with the ability to create clear product specs, user stories, and stakeholder updates for both technical and non-technical audiences.
Strong analytical and problem-solving skills, with a focus on measurable outcomes and data-driven decision-making.
Ability to operate cross-functionally and influence engineering, data, design, and business stakeholders.

Nice To Haves

Hands-on experience with computer vision applications such as object detection, image classification, or video analysis in real-world environments, preferred.
Experience building or integrating multimodal AI features that combine vision and language capabilities, preferred.
Experience applying AI to improve customer experiences or internal operations (e.g., automation, support workflows, or decision support systems), preferred.
Familiarity with MLOps tools, dataset curation, and data labeling workflows, preferred.
Experience with edge or on-device inference, mobile AI integration, or IoT/device-based systems, preferred.
Experience tuning model outputs (e.g., thresholds, prompts, business logic) to balance accuracy, user experience, and operational impact, preferred.
Background in consumer technology, connected devices, or related product categories, preferred.

Responsibilities

Own the product roadmap for AI platform capabilities, including computer vision (image and video analysis, detection, classification, and scene understanding) and broader AI features such as language-based systems, multimodal experiences, and intelligent automation.
Lead day-to-day collaboration with third-party AI partners (model providers, MLOps platforms, and edge/embedded solutions), including scoping, requirements, model evaluation, tuning, and acceptance.
Define scalable integration patterns for incorporating AI capabilities into internal systems and customer-facing products, including APIs, data pipelines, inference workflows, and performance trade-offs (latency, cost, reliability).
Partner with engineering teams across software, hardware, and firmware to design and deliver services and SDKs that enable AI-powered functionality across applications, devices, and internal tools.
Collaborate with business stakeholders to identify high-impact opportunities for AI, balancing performance, cost, and user experience considerations.
Write clear user stories, acceptance criteria, and technical specifications; manage backlog prioritization and lead agile ceremonies for the AI platform workstream.
Define and track product and model performance metrics, including quality, accuracy, latency, cost efficiency, user adoption, and operational impact.
Lead model evaluation efforts using curated datasets and evaluation frameworks, incorporating real-world edge cases and diverse operating conditions.
Partner with data and engineering teams to ensure responsible data collection, governance, and usage, including privacy and compliance considerations.
Collaborate with UX, Marketing, Customer Support, and Operations teams to shape how AI-driven capabilities are delivered and experienced, including feedback loops and continuous improvement mechanisms.
Support vendor evaluation, selection, and ongoing management, including commercial considerations and service-level expectations.
Stay current on advancements in AI, computer vision, and machine learning, and translate emerging capabilities into actionable product opportunities.