Machine Learning Engineer

Job Title: MLOps Engineer Duration: 12 Month(s) Location: Cincinnati, OH or Remote Information • This is contract only, with the possibility to extend • On-site (at the BTD) and remote candidates will be considered • Pre-screen consists of 5 video questions and a games section 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.