Machine Learning Operations Engineer/Senior

Job Overview

The Machine Learning Operations Engineer/Senior is responsible for the design, development, deployment, monitoring, and operationalization of machine learning and AI solutions. The engineer works closely with Business Stakeholders, Data Analysts, Data Scientists, and ML Engineers within Analytics Exploration to transform business opportunities into scalable, production-grade AI and machine learning solutions. The role collaborates with Software Engineers, Data Engineers, Platform Engineers, and Enterprise Architecture teams to ensure robust infrastructure, governed data pipelines, and secure MLOps practices that enable reliable deployment and management of AI and machine learning models across the enterprise.

Job Responsibilities

• Partner with Business Stakeholders and Analytics Exploration teams to identify, prioritize, and operationalize AI and machine learning use cases that deliver measurable business value.
• Design, implement, and maintain enterprise MLOps frameworks supporting model training, deployment, monitoring, governance, and lifecycle management.
• Develop and support machine learning solutions using Databricks, MLflow, Unity Catalog, Feature Store, and other cloud-native AI platforms.
• Implement model monitoring capabilities including data drift, model drift, performance monitoring, explainability, and alerting.
• Establish CI/CD and automated deployment pipelines for machine learning and generative AI solutions.
• Ensure adherence to Model Risk Management (MRM), Responsible AI, data governance, security, and regulatory compliance requirements.
• Collaborate with Platform Engineering and Architecture teams to define standards, patterns, and best practices for AI/ML solution deployment.
• Support the evaluation and adoption of emerging AI technologies, including Generative AI, LLMOps, Agentic AI, and advanced analytics capabilities.

Qualifications

• More than 5 years of experience in developing and deploying enterprise-scale machine learning solutions.
• Hands-on experience with Databricks Lakehouse Platform, including Databricks Workflows, MLflow, Unity Catalog, Model Serving, Feature Store, and Mosaic AI capabilities.
• Experience implementing end-to-end MLOps solutions on Databricks, AWS SageMaker, or comparable enterprise machine learning platforms.
• Knowledge of model governance, model lifecycle management, Responsible AI practices, and Model Risk Management (MRM) frameworks.
• Experience building and managing CI/CD pipelines for machine learning workloads using tools such as GitHub Actions, Azure DevOps, Jenkins, or equivalent.
• Experience monitoring machine learning models for data drift, concept drift, model performance degradation, and operational reliability.
• Familiarity with Generative AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), prompt engineering, and LLMOps practices.
• Experience working with cloud-based data and analytics platforms, including AWS services such as S3, Glue, Lambda, SageMaker, EKS, and related technologies.
• Strong understanding of data governance, security, access controls, metadata management, and auditability within enterprise AI platforms.
• Experience collaborating directly with Business Stakeholders to translate business objectives into AI and machine learning solutions.
• Experience working within cross-functional teams including Data Science, Analytics Exploration, Data Engineering, Software Engineering, Platform Operations, and Architecture.
• Experience implementing enterprise AI platforms using Databricks on AWS.
• Experience supporting insurance, financial services, or other highly regulated industries.
• Experience with GenAI, Agentic AI, RAG architectures, vector databases, and AI governance frameworks.
• Experience establishing enterprise MLOps standards, monitoring frameworks, and operational support models.
• Advanced degree in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related field 

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