Role Summary
We are seeking an experienced MLOps Engineer to support a high impact enterprise AI initiative focused on intelligent task prioritization across a large retail network. This role will play a critical part in designing, optimizing, and operationalizing machine learning systems that help drive real time decision making and improve workforce efficiency.
The ideal candidate will work closely with Data Scientists and Software Engineers to build scalable MLOps capabilities, optimize inference performance, and establish reliable production workflows. You will help bridge the gap between model development and operational deployment, ensuring machine learning solutions are scalable, efficient, and aligned with business objectives.
Key Responsibilities
• Design and implement scalable MLOps pipelines that support enterprise AI and machine learning initiatives
• Develop, deploy, and maintain machine learning workflows within Google Cloud Platform and Vertex AI
• Optimize inference systems to improve model performance, scalability, reliability, and operational efficiency
• Support large scale batch inference processes for machine learning workloads and predictive models
• Partner closely with Data Science and Engineering teams to operationalize machine learning solutions
• Improve machine learning deployment processes and establish best practices for model lifecycle management
• Support ML workloads that process and prioritize millions of records across a large distributed environment
• Monitor, troubleshoot, and enhance machine learning infrastructure, workflows, and production systems
Key Requirements
• 5+ years of experience in MLOps, Machine Learning Engineering, Data Engineering, or related technical roles
• Strong hands on experience designing, implementing, and supporting production MLOps pipelines
• Proven expertise with Google Cloud Platform and Vertex AI
• Advanced proficiency in Python and machine learning workflow automation
• Experience optimizing inference systems and supporting high volume production machine learning environments
• Strong experience with batch inference, model deployment, monitoring, and operational support
• Proficiency with BigQuery and large scale cloud based data processing environments
• Excellent collaboration, communication, problem solving, and stakeholder management skills
Preferred Qualifications
• Experience with TensorFlow and modern machine learning frameworks
• Knowledge of Dataform and data transformation workflows
• Experience processing and managing datasets containing millions of records
• Familiarity with Spark, PySpark, and distributed data processing technologies
• Experience working with AI agents, agentic workflows, or Google Agent Development Kit technologies
• Experience supporting enterprise scale AI, machine learning, or predictive analytics initiatives
Why Join This Opportunity
• Contribute to a highly visible AI initiative with enterprise wide impact
• Work with cutting edge machine learning and cloud technologies
• Collaborate directly with Data Science and Engineering teams in a highly innovative environment
• Influence the design and evolution of scalable MLOps practices and architectures
• Gain exposure to large scale AI workloads that drive operational decision making across thousands of locations
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