ML Ops Engineer

Job Title: MLOps Engineer

Description: We are seeking an experienced MLOps Engineer to join our team and play a key role in deploying, automating, monitoring, and optimizing machine learning solutions. The ideal candidate will have strong experience with Databricks, SQL, Python, PySpark, and CI/CD pipelines, along with hands-on exposure to Jenkins, SonarQube, Jira, and Git.

You will collaborate with data scientists, data engineers, and DevOps teams to streamline the ML lifecycle from model development to production deployment and monitoring.

Key Responsibilities
•    Design, implement, and maintain MLOps pipelines for machine learning model deployment and monitoring.•    Collaborate with data scientists to package and deploy ML models in Databricks environments.•    Develop scalable data processing pipelines using PySpark and SQL.•    Implement CI/CD pipelines for ML workflows using Jenkins and ensure code quality with SonarQube.•    Integrate ML workflows with version control systems (Git) and project management tools (Jira).•    Automate data ingestion, feature engineering, model training, evaluation, and deployment processes.•    Monitor deployed models for performance drift, accuracy, and operational issues.•    Troubleshoot, optimize, and refactor ML pipelines for better efficiency and reliability.•    Enforce coding standards, security best practices, and compliance requirements.•    Experience in Lake House monitoring for model monitoring and feature store•    Orchestration of ML jobs jobs in Jenkins

Required Skills & Qualifications
•    3+ years of experience as an MLOps Engineer, Data Engineer, or similar role.•    Strong expertise in Databricks for model deployment and data engineering.•    Proficiency in Python and PySpark for data processing and ML workflow automation.•    Solid SQL skills for querying and manipulating structured data.•    Hands-on experience with CI/CD tools like Jenkins.•    Experience with SonarQube for code quality checks.•    Familiarity with Git for version control and Jira for task/project management.•    Understanding of ML model lifecycle, including training, validation, deployment, and monitoring.•    Strong problem-solving skills and ability to work in cross-functional teams.

Preferred Skills
•    Experience with AWS, Azure, or GCP for cloud-based ML solutions.•    Exposure to MLflow for experiment tracking and model registry.•    Understanding of security best practices in ML pipelines.