IT- Product Owner: III (Senior)

Salary: $59.00 to $64.00 hourly

Description

About the Role

We are seeking a highly skilled technical engineer for the role of Technical Product Owner (Machine Learning Operations) in a 90-day contract-to-permanent capacity, hybrid, located in Columbus, OH.

This is not a traditional business-focused product owner role -- we need someone with deep technical experience in MLOps, cloud infrastructure, DevOps, and CI/CD pipelines. While formal product management experience is a plus, we prefer engineers with strong technical foundations who are eager to step into a product leadership role.

If you are a product owner with very deep relevant (see more below) technical expertise, we are open to having a conversation.

This Role Is Ideal For
• Experienced MLOps engineers, DevOps engineers, or ML engineers looking to transition into technical product management.
• Current product owners with strong MLOps and DevOps expertise.

Key Responsibilities

Onboarding & Training
• Align with internal product owner methodologies and governance.
• Familiarize with MLOps workflows, DevOps pipelines, and platform architecture.
• Learn Infrastructure-as-code best practices (Terraform, Kubernetes, AWS).
• Complete refresher training on MLOps frameworks, CI/CD pipelines, Terraform, and AWS SageMaker.

Product Ownership & Backlog Management
• Collaborate with data scientists, engineers, and business users to define machine learning and analytics requirements.
• Own and prioritize backlog in Azure DevOps (ADO) ensuring clarity and traceability.
• Lead discovery sessions to define ROI, scope, and ‘Definition of Done’ for solutions.
• Translate business needs into structured requirements for Solution Engineers and ensure model deployment integration (batch, real-time, LLMs).

Solution Engineering & Implementation
• Act as a bridge between engineering and business, translating technical challenges into actionable items.
• Collaborate with Solution Engineering, Cyber teams, architects, and Implementation Engineers for design and deployment.
• Work with Production Support to define monitoring, alerting, and incident management.
• Ensure proper routing of model outputs (Data Lake, Kafka, BigQuery, Apigee Gateway).

Governance, Monitoring & Incident Management
• Define model drift, data drift detection, and Model Risk Management (MRM) requirements.
• Ensure solutions meet MRM standards related to model metadata (KPIs) and governance.
• Establish incident tracking workflows via ServiceNow, removing reliance on email alerts.
• Enforce CI/CD best practices for automated model deployment and monitoring.

Requirements

Qualifications & Required Experience
• 7+ years of hands-on experience in MLOps, ML Engineering, DevOps, or Data Engineering.
• Experience in an ML setting is mandatory. Pure DevOps or Data Engineering without ML context is not what we are looking for.

Either
• Previous product ownership experience in an MLOps or DevOps-focused team.
• OR An experienced MLOps engineer looking to transition into product management.

Deep technical expertise in the following.
• Be able to write code (primarily Python, Terraform) when necessary.
• CI/CD pipelines, DevOps automation, and Site Reliability Engineering (SRE) best practices.
• Cloud-native ML infrastructure (AWS, S3, Lambda, EKS, EventBridge, SNS, SQS, Kafka, Event Hub, BigQuery, Apigee).
• Infrastructure-as-code (Terraform, Kubernetes, Docker).
• Should have worked on any of the open source MLOps frameworks (Shakudo, MLflow, DVC, Great Expectations, Airflow, KServe, Kubeflow).
• Amazon SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor, Endpoints).

Expertise In Azure DevOps (ADO), Including
• Boards (Epics, Features, Stories, Tasks).
• Repos (Code management, branching, pull requests).
• Pipelines (CI/CD automation).
• Strong experience working with data scientists to translate ML requirements into production-ready solutions.
• ServiceNow and enterprise incident management experience.

Why Join Us?
• Opportunity to be hands on in MLOps maturity journey. Deep exposure to cloud-native AI/ML infrastructure and open-source MLOps tools.
• Unique opportunity for engineers to transition into product management in a structured and high-impact environment.
• Immediate contributions to enterprise-scale MLOps initiatives.
• 90-day contract-to-hire with a clear path to full-time conversion.
• Work on cutting-edge AI/ML deployments across marketing, risk, and financial optimization.

Innovation starts with people.®

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