Job Title -Technical Product Owner- Machine Learning Operations
Location -Columbus, Ohio
Duration -12 Month
Proper LinkedIN
Hybrid
3 days a week in the office
Columbus, OH only
Job Description :
Expectations as a contractor : We need the resource to be onsite in Columbus office from day-1 of the contract. 3 days a week in the office is our expectation.
About the Role
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. If you are a product owner with very deep relevant (see more below) technical expertise, we are open to having a conversation. In either case, you need to be able to articulate your deep experience in the form of responses to screening questions. I expect detailed responses instead of 1-2 lines for each question.
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. There will be an assessment test after 30-days to ensure the right fit.
Key Responsibilities
1. 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 frameworksCI / 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.2. 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.3. 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).4. 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.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.Muskan Sharma Sr. IT Recruiter
Email- muskan@stellentit.com
Phone Number : 2015841186
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