| Location | Westbrook, ME (Remote) |
| Salary | $55–$66.43 Per Hour |
Technical Product Owner AI Delivery
Job Summary:
We are seeking an experienced Technical Product Owner to join the **** at **** to own the delivery of AI/ML models for a defined product cluster. This role is the primary bridge between product-level priorities set by the Product Manager and the technical work of a cross-functional team of data scientists, ML engineers, and data engineers. You will own the AI Layer backlog for your product cluster managing model development workstreams, making experiment scope and continuation decisions, and ensuring a clean DS-to-MLE productionization handoff. You will partner closely with a Tech Leads (DS and MLE) who own technical feasibility and other POs who own external technical dependency resolution.
What you will do:
Own and maintain a prioritized AI Layer backlog for the assigned product cluster, with DS and MLE work represented as distinct, sequenced backlog items.
Translate product-level priorities from the Product Manager into AI Layer workstreams with clear, technically specific acceptance criteria for both DS and MLE work.
Write acceptance criteria for DS work (model performance thresholds, evaluation methodology, holdout set specification, model card completeness) and MLE work (serving latency SLOs, monitoring requirements, rollback procedures) separately.
Own the DS-to-MLE Handoff Review ceremony: ensure model readiness criteria including eval documentation, serving requirements, and monitoring criteria are fully met before MLE operationalization work enters a sprint.
Partner with the Tech Lead at every backlog refinement to validate feasibility, surface technical risks, and confirm story scope and sizing before sprint commitment.
Make sprint-level trade-off decisions scope, quality threshold, experiment continuation or termination with authority and appropriate speed.
Facilitate sprint planning, backlog refinement, sprint demo, and retrospective ceremonies for the assigned team.
Surface external dependency blockers to the appropriate owner immediately.
Shield the team from unplanned work and context-switching by enforcing backlog discipline and managing stakeholder expectations.
Continuously improve team leverage through AI, agents, and workflow automation.
Automate routine delivery-management activities including backlog refinement, reporting, dependency tracking, and handoff validation where appropriate.
Measure and report efficiency gains from AI-enabled delivery practices.
What you need:
Proven experience as a Product Owner or Product Manager for AI/ML or data science product delivery.
Direct experience working with cross-functional teams of data scientists and ML engineers in a production ML environment.
Ability to read and interpret model evaluation results, experiment logs, and ML pipeline outputs as an informed decision-maker not necessarily as a practitioner.
Demonstrated ability to write technically specific acceptance criteria for both DS model work and MLE productionization work.
Experience distinguishing and sequencing research/experimentation work from engineering/production work in a backlog.
Experience with the DS-to-MLE model productionization handoff: what constitutes a complete handoff, what risks arise from incomplete ones.
Strong prioritization and trade-off decision-making skills in an environment of high technical uncertainty.
Ability to work effectively across multiple tasks and teams while meeting aggressive timelines.
Outstanding written and verbal communication skills; able to explain ML model trade-offs and delivery risks to non-technical stakeholders.
Education and Experience:
Bachelor's degree or above in Computer Science, Data Science, Statistics, or related field; Master's degree preferred.
2 3 years of experience in roles as Product Owner, Product Manager, or technical delivery lead for AI/ML or data science products.
Demonstrated track record of shipping ML models into production across the full lifecycle: from problem framing through monitoring.
Additional Desirable Experience:
Background in data science, machine learning, or a quantitative field.
Familiarity with ML experiment tracking and model lifecycle tooling.
Experience with real-time inference products and latency-sensitive ML serving requirements.
Prior experience owning or managing an ML platform or model serving infrastructure.
Experience with Databricks or equivalent cloud ML platform.