MLOps Engineer(Local to OH)

Job Description

We are seeking a highly skilled MLOps Engineer to build, scale, and support our Google Vertex machine learning platform to enable a multi-model serving environment. This role is central to building reusable infrastructure components (i.e., infra as code), model deployment pipelines, and provide a collection of templates that accelerate model deployment, monitoring, and support for domain teams.
This contractor will collaborate closely with data scientists, other MLOps engineers, and product teams to ensure that models are deployed efficiently that are observable, maintainable, and aligned with business goals.
This work will empower domain teams to independently run, support, and monitor their models using platform-provided tools and best practices.
This role is part of a larger ML platform team that supports Kroger s product recommendation capabilities.
This role will work alongside Data Scientist, Data Engineers, Machine Learning Engineers, and software engineers to build, test, maintain, and support data pipelines, ML Models, and back-end services that make up our product recommender platform.

Qualifications:

* Bachelor's or Master s degree in computer science, Engineering, or related field.

* Minimum of 4 years of experience in MLOps, with a demonstrated ability to work with various ML platforms.

* Strong proficiency in Python and familiarity with data science methodologies.

* Experience with cloud technologies, particularly Google Cloud and Vertex AI, and adaptability to technologies like Microsoft Azure or open-source tools.

* Maintain expertise in a range of ML technologies and platforms, with a preference for Google Vertex AI, but open to other systems as needed.

* Leverage support for open-source frameworks like TensorFlow, PyTorch, scikit-learn, and integrate them with ML frameworks via custom containers.

* Excellent communication skills, capable of bridging technical and business domains.

Preferred Skills:

* Experience working collaboratively with data science teams, understanding their needs and challenges.

* Ability to lead initiatives and communicate effectively with technical teams and senior leadership.

* Familiarity with a broad range of ML tools and frameworks, and openness to adapting to emerging technologies.

Key Responsibilities

* Design and implement reusable modules and templates for model training, deployment, and monitoring across Vertex AI and other cloud platforms.

* Build and maintain scalable CI/CD pipelines for ML workflows, enabling rapid iteration and safe promotion across environments.

* Develop tooling and automation to support overall platform observability, including drift detection, performance tracking, request latency, and alerting.

* Partner with domain teams to onboard models into the platform, ensuring alignment with operational standards and SLAs.

* Maintain and evolve the feature store, model registry, and endpoint management systems to support high-throughput, low-latency inference.

* Collaborate with leadership to define and enforce governance policies, including versioning, rollback strategies, and access controls.

* Provide technical guidance and support to domain teams, enabling self-service capabilities and reducing operational bottlenecks.

* Work closely with data scientists to understand their needs and efficiently integrate their models into production systems.

* Act as a liaison between the data engineering, data science, MLEs, MLOps, and leadership teams, facilitating seamless communication and goal alignment.