Technical Product Owner -- Machine Learning Operat (1076631)

Location: Columbus, OH

Salary: Negotiable

Description

JOB DESCRIPTION:

Job Title:Machine Learning Operations (MLOps)

Location: Columbus, OH (3 days onsite, 2 days remote)

Type: 3+ Months Contract To Hire

Contract – Only W2

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.

The first 30 days include structured onboarding and refresher training to align with our internal frameworks, DevOps/MLOps best practices, and enterprise expectations. This is not entry-level training, but a targeted upskilling for engineers transitioning into product management.

Key Responsibilities
• Transition & Structured Onboarding (First 30 Days).

Participate In Structured Onboarding To Align With
• 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.

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.
• Work on cutting-edge AI/ML deployments across marketing, risk, and financial optimization.

Contact: smishra02@judge.com

This job and many more are available through The Judge Group. Find us on the web at www.judge.com