ML Ops Engineer

Not available for C2C engagements Vendors marketing candidates will be blocked Must have experience: Strong MLOps experienceDeep expertise with Vertex AI (GCP)Experience building and optimizing ML inference systemsExperience creating and maintaining MLOps pipelinesStrong PythonExperience with TensorFlowExperience with BigQueryExperience operating at large scale datasets (millions+ records)Batch inference experienceMLOps EngineerCompany OverviewThe Intersect Group is partnering with an innovative organization in the retail technology space that is transforming how work gets done at scale through artificial intelligence and advanced analytics. This organization is focused on improving operational efficiency and empowering frontline teams with intelligent decision making tools that drive meaningful business outcomes. Their collaborative culture brings together data, technology, and business expertise to solve complex challenges and deliver measurable impact across a large enterprise environment.Role SummaryWe are seeking an experienced MLOps Engineer to support a large scale AI initiative designed to intelligently prioritize tasks across approximately 2,000 retail locations. This role will play a critical part in building, optimizing, and maintaining the machine learning infrastructure that enables real time operational decision making.Working closely with Data Scientists and Engineering teams, you will design and implement scalable MLOps solutions, optimize inference systems, and support high volume machine learning workloads. The ideal candidate is passionate about operationalizing machine learning, improving collaboration across technical teams, and driving the successful deployment of AI solutions in production environments.Key Responsibilities• Design and implement scalable MLOps pipelines supporting enterprise AI initiatives• Develop and maintain machine learning workflows within Google Cloud Platform and Vertex AI• Optimize inference systems for performance, scalability, reliability, and cost efficiency• Support and enhance batch inference processes for large scale machine learning workloads• Collaborate closely with Data Scientists and Software Engineers throughout the ML lifecycle• Build processes that improve alignment and collaboration between Data Science and Engineering teams• Monitor, troubleshoot, and improve production ML systems supporting thousands of retail locations• Identify opportunities to enhance machine learning operations, automation, and platform performanceKey Requirements• 5+ years of experience in MLOps, Machine Learning Engineering, or a related field• Hands on experience designing, implementing, and supporting production grade MLOps pipelines• Strong experience with Vertex AI and Google Cloud Platform (GCP)• Advanced Python development experience within machine learning environments• Experience optimizing inference systems and supporting batch inference workloads• Strong experience with BigQuery and large scale data processing environments• Experience supporting machine learning solutions operating against high volume datasets and enterprise scale workloads• Proven ability to collaborate effectively with Data Scientists, Engineers, and cross functional stakeholdersPreferred Qualifications• Experience with TensorFlow• Experience with Spark or PySpark• Familiarity with Pataform• Experience working with datasets containing millions of records• Exposure to AI agents and Google Agent Development Kit (ADK)Why ApplyThis is an opportunity to contribute to a highly visible AI initiative that will directly impact operational effectiveness across thousands of locations. You will work alongside talented Data Science and Engineering professionals while helping shape the future of machine learning operations within a large scale enterprise environment.Apply TodayIf you are an experienced MLOps Engineer excited about building scalable AI solutions and driving real business impact, we would love to hear from you. Please submit your resume and contact information to The Intersect Group to be considered for this opportunity.