Senior MLOps Engineer

Job description

Accountabilities: You will be responsible for building and maintaining scalable ML infrastructure and ensuring the reliability of production ML systems. This includes designing robust pipelines and enabling seamless model deployment and monitoring. Build and maintain ML infrastructure on Databricks, leveraging Unity Catalog and feature stores for scalable model development and deployment. Design and implement drift detection frameworks to continuously monitor data and model performance in production. Develop model calibration and versioning systems to ensure reproducibility, traceability, and governance across ML lifecycle stages. Architect low-latency reinforcement learning orchestration pipelines, including contextual bandits and Q-learning models for real-time decisioning. Create automated training, validation, and retraining pipelines to support efficient experimentation and continuous model improvement. Implement CI/CD pipelines for ML workflows using Git-based workflows and Databricks integrations. Build monitoring and observability tools to track model performance, operational metrics, and drift mitigation strategies. Collaborate closely with ML scientists and engineering teams to deploy and maintain production-grade models. Requirements: The ideal candidate has strong experience in production ML systems and deep expertise in MLOps tooling, cloud data platforms, and automation practices. 7+ years of experience in MLOps, ML engineering, or related production-focused ML roles. Strong hands-on expertise with Databricks, Apache Spark, MLflow, Unity Catalog, and feature store architectures. Proven experience designing and deploying ML monitoring systems, including drift detection and performance tracking. Strong knowledge of reinforcement learning approaches such as contextual bandits and Q-learning in production environments. Experience building automated ML training and retraining pipelines for scalable model lifecycle management. Deep understanding of CI/CD principles, Git workflows, and tools such as GitLab or similar platforms. Strong programming skills in Python and SQL, with experience in large-scale data processing using Spark. Familiarity with orchestration and ML lifecycle tools such as Kubeflow and Airflow. Strong analytical thinking, problem-solving skills, and ability to work in cross-functional teams. Benefits: Opportunity to work on cutting-edge ML systems in a high-impact, production-driven environment. Exposure to modern data platforms such as Databricks and advanced ML lifecycle tooling. Work on real-time, low-latency machine learning and reinforcement learning applications. Collaborative, innovation-driven engineering culture with strong focus on scalability and reliability. Opportunities for continuous learning and professional growth in advanced ML engineering domains. Flexible and remote-friendly work arrangements (depending on team structure). Inclusive and diverse work environment encouraging innovation and technical excellence. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1 This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior MLOps Engineer in India. This role is designed for an experienced MLOps professional who will help operationalize and scale machine learning systems powering dynamic pricing and highly personalized user experiences. You will work at the intersection of ML engineering and production infrastructure, building resilient systems that ensure models remain accurate, reliable, and continuously improved in real time. The position focuses heavily on designing end-to-end ML pipelines, from training and deployment to monitoring and retraining. You will contribute to advanced ML capabilities such as drift detection, model calibration, and reinforcement learning orchestration. Working in a fast-paced, data-driven environment, you will collaborate closely with ML scientists and engineers to bring complex models into production. Your work will directly impact business performance through scalable, low-latency ML systems built on modern cloud and big data technologies.