W2 Contract to Hire Req: MLOps Technical Product Owner (Hybrid / Columbus,OH)

Job Title: MLOps Technical Product Owner

Location: Hybrid / Columbus, OH

Job Type: Contract to Hire

We are seeking a highly technical engineer for the role of Technical Product Owner -- Machine Learning Operations (MLOps) in a 90-day contract-to-hire capacity.

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

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

Transition & Structured Onboarding (First 30 Days).

Participate in structured onboarding to align with:

• Huntington’s internal product owner methodologies and governance.

• Enterprise-specific MLOps workflows, DevOps pipelines, and platform architecture.

• Infrastructure-as-code best practices (Terraform, Kubernetes, AWS cloud-native deployments).

Complete targeted refresher training on:

• MLOps frameworks

• CI/CD pipelines, Terraform, and DevOps automation.

• AWS SageMaker workflows, feature stores, and model monitoring.

Begin owning backlog, conduct discovery sessions and start owning the requirement gathering responsibilities from Week 1 while completing technical refreshers.

Product Ownership & Backlog Management

• Work closely with data scientists, engineers, and business users to define requirements for machine learning models and analytics pipelines.

• Own and refine the backlog in Azure DevOps (ADO) ensuring clarity, prioritization, and traceability.

• Conduct deep discovery conversations to define ROI, project scope, and ‘Definition of Done’ for machine learning and analytics solutions.

• Translate engineering needs into structured product requirements while considering scalability, automation, and operational efficiency.

• Translate business needs into very detailed structured requirements for Solution Engineers.

• Ensure model deployment requirements (batch, real-time, LLMs) are well-defined and integrated into downstream systems.

Solution Engineering & Implementation Collaboration

• Bridge the gap between engineering and business, translating technical challenges into actionable backlog items.

Collaborate with:

• Solution Engineering Team, Cyber teams and architects for architectural design.

• Implementation Engineering Team for solution deployment.

• Production Support Team to define monitoring, alerting, and incident management.

• Machine Learning Engineering Team to drive platform enhancements.

• Ensure model outputs are correctly routed (Data Lake, Kafka Event Hub, BigQuery, Apigee Gateway).

Governance, Monitoring & Incident Management

• Define and document model drift and data drift detection requirements along with Model Risk Management (MRM) requirements.

• Ensure the solution meets and exceeds MRM expectations related to Model’s metadata (KPIs) and governance.

• Ensure robust incident tracking workflows via ServiceNow, eliminating reliance on email-based alerts.

• Work with engineers to enforce CI/CD best practices for automated model deployment and monitoring.

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. We expect you to 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.