ole : AI / ML Ops Engineer Location: Princeton, NJ (Hybrid – 3 days onsite required) Type - Contract Experience Required: Minimum 12+ years (targeting strong senior profiles) Role Overview: We are seeking a highly skilled ML Ops Engineer with a strong background in Azure Cloud DevOps and extensive experience in Databricks, particularly custom Databricks Asset Bundles (DABs) implementation. The ideal candidate will be responsible for developing, deploying, and maintaining scalable machine learning solutions in a production environment, with a clear focus on operationalizing ML models using modern DevOps practices. Key Responsibilities: 🔹 Core Responsibilities: • Develop and manage CI/CD pipelines for ML model deployment using Azure DevOps. • Design, implement, and maintain custom Databricks Asset Bundles (DABs) for efficient ML workflow management. • Collaborate with data scientists to operationalize ML models across various stages from development to production. • Write clean, modular, and efficient Python/PySpark code for ETL and ML operations. • Build and maintain data pipelines using Azure Data Factory (ADF) and Databricks. • Implement end-to-end MLOps architecture to support multiple models in a scalable and automated manner. • Conduct quality assurance and testing for data science solutions. Required Skills & Experience: • 5–7+ years as ML Engineer, with proven experience in ML model deployment & MLOps. • Strong expertise in Azure Cloud engineering and services. • Hands-on experience in Azure DevOps, CI/CD pipelines, and git-based workflows. • Expert in Databricks and custom DABs (Databricks Asset Bundles) implementation. • Proficient in Python and PySpark for ML workflows and data engineering tasks. • Skilled in building and maintaining ETL pipelines using Azure Data Factory and Databricks. Good to Have: • Experience with Databricks Lakehouse architecture, Delta Lake, and Delta Live Tables. • Familiarity with Databricks Unity Catalog and Databricks SQL. • Strong understanding of Lakehouse optimization and real-time/streaming data pipelines. • Knowledge of Agile development and DevOps best practices within Azure. • Ability to translate business requirements into scalable ML solutions. • Experience in fraud detection models and other risk mitigation use cases. • Proven ability to design scalable MLOps architectures for multiple model deployments. • Competence in monitoring and managing ML lifecycle using high-end tools and frameworks. • Strong communication and stakeholder collaboration skills. Ideal Candidate Profile: • 12+ years of experience in software/data engineering, with at least 5–7 years in MLOps & Azure DevOps. • Local to New Jersey or nearby, with ability to commute to Princeton, NJ (3 days onsite). • Deep hands-on expertise in both machine learning engineering and cloud DevOps pipelines. • Strong understanding of data architecture, model lifecycle management, and ML systems monitoring. Keywords for Sourcing: • MLOps, Azure Cloud, Azure DevOps, Databricks, DABs, ADF, Python, PySpark, Delta Lake, Delta Live Tables, Unity Catalog, CI/CD, ETL, Lakehouse, Machine Learning Deployment, ML Lifecycle, DevOps for ML