Job Title: MLOps Engineer Location: Onsite – Berkeley Heights, NJ Job Summary: We are seeking a skilled MLOps Engineer with hands-on experience in Azure Databricks, Python, and MLOps best practices. You will be responsible for designing, building, and maintaining scalable ML pipelines and deployment infrastructure to streamline the end-to-end machine learning lifecycle. Key Responsibilities: • Develop and manage scalable ML pipelines using Azure Databricks and related services. • Automate and streamline the model training, validation, deployment, and monitoring processes. • Collaborate with data scientists and engineers to productionize ML models. • Implement CI/CD pipelines for ML workflows using tools like Azure DevOps or GitHub Actions. • Optimize data ingestion, feature engineering, and transformation processes in Databricks. • Monitor model performance and implement retraining pipelines as needed. • Ensure compliance with model governance, security, and reproducibility standards. • Contribute to building reusable MLOps frameworks and tools across teams. Required Skills & Experience: • 8+ years of experience in MLOps, data engineering, or ML infrastructure roles. • Strong hands-on experience with Azure Databricks, including notebooks, jobs, and MLflow. • Proficient in Python for scripting, automation, and ML-related tasks. • Experience with MLOps frameworks (e.g., MLflow, TFX) and pipeline orchestration tools (e.g., Airflow, Azure ML Pipelines). • Experience with containerization (Docker) and version control (Git). • Familiarity with cloud-based ML model deployment and monitoring best practices. • Solid understanding of machine learning lifecycle and DevOps principles. Preferred Skills: • Experience with Azure ML, Azure DevOps, or Terraform for infrastructure automation. • Familiarity with Delta Lake, Spark, and data lake architectures. • Exposure to model explainability, governance, and compliance tools. • Strong communication and collaboration skills.