Senior MLOps Engineer

Job Description

• This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior MLOps Engineer based in India. This is a high-impact opportunity to build the infrastructure that turns advanced machine learning models into reliable, production-ready systems. You will operationalize ML workflows supporting dynamic pricing and personalized consumer experiences at scale. The role combines MLOps engineering, cloud-based data platforms, automated model training, monitoring, and low-latency model orchestration. You will develop robust frameworks for drift detection, model calibration, versioning, retraining, and production monitoring. Your work will help ML teams deploy models efficiently while maintaining reliability, reproducibility, performance, and operational visibility. You will collaborate closely with ML Scientists and engineering stakeholders in an environment focused on automation, experimentation, and continuous improvement. This role is ideal for an experienced MLOps professional who enjoys solving complex infrastructure challenges and building scalable ML systems from development through production.
• Accountabilities: ML Infrastructure: Build, maintain, and scale machine learning infrastructure using Databricks, Unity Catalog, feature stores, and related technologies to support the complete ML lifecycle. Drift Detection: Design and implement robust frameworks for detecting data and model drift, enabling proactive monitoring and reliable production performance. Model Calibration & Versioning: Develop model calibration frameworks and establish strong versioning practices that support transparency, reproducibility, and controlled model releases. ML Orchestration: Design and optimize orchestration pipelines for low-latency ML models, including reinforcement learning approaches such as Contextual Bandits and Q-learning. Automated Training: Build automated pipelines and frameworks for model training, retraining, validation, and deployment, improving experimentation speed and operational efficiency. CI/CD for ML: Implement and maintain CI/CD practices for machine learning workflows, integrating Git-based development, Databricks workflows, and automated deployment processes. Production Monitoring: Develop monitoring and operational analytics capabilities to track model performance, identify degradation, and support effective drift mitigation and retraining. Model Lifecycle Management: Establish reliable processes covering model development, testing, deployment, monitoring, versioning, and retirement. ML Scientist Collaboration: Partner closely with ML Scientists to productionize, deploy, operate, and maintain machine learning models and experimentation workflows. Operational Optimization: Continuously improve ML infrastructure, pipelines, and workflows to increase scalability, reliability, efficiency, and deployment velocity. Requirements Professional Experience: 7+ years of experience in MLOps, ML Engineering, or closely related roles, with substantial experience deploying and managing machine learning workflows in production. MLOps Expertise: Proven experience building drift detection systems, model calibration frameworks, monitoring solutions, automated retraining workflows, and other production ML infrastructure. Databricks Ecosystem: Strong hands-on experience with Databricks, Apache Spark, MLflow, Unity Catalog, and feature stores. ML Orchestration: Experience deploying and orchestrating low-latency machine learning models, including reinforcement learning solutions such as Contextual Bandits and Q-learning. Training Automation: Strong experience designing automated ML training, validation, retraining, and deployment pipelines with a focus on efficiency and reliability. CI/CD & Git: Strong understanding of Git workflows, CI/CD practices, and tools such as GitLab or equivalent platforms. Programming & Data: Proficiency in Python and SQL, along with strong experience processing large-scale data using Apache Spark or similar technologies. ML Lifecycle Tools: Familiarity with tools such as MLflow, Kubeflow, and Airflow for experiment tracking, workflow orchestration, and ML lifecycle management. Monitoring & Reliability: Deep understanding of model performance monitoring, data and model drift, retraining strategies, and production reliability. Problem-Solving: Strong analytical and troubleshooting abilities, with a proactive approach to identifying infrastructure and model lifecycle challenges. Collaboration: Excellent communication skills and the ability to work effectively with ML Scientists and cross-functional engineering teams. Scalability Mindset: Ability to design robust, automated, and scalable systems capable of supporting complex machine learning workloads in production. Benefits Opportunity to work on advanced ML infrastructure supporting dynamic pricing and personalized consumer experiences. Exposure to modern technologies including Databricks, Spark, MLflow, Unity Catalog, feature stores, and reinforcement learning workflows. High-impact role with ownership across the full machine learning lifecycle, from training and experimentation through deployment and monitoring. Collaborative environment with close interaction with ML Scientists and engineering teams. Opportunity to build scalable automation and infrastructure that directly improves model reliability and deployment efficiency. Competitive compensation aligned with experience and market standards. Professional development opportunities through exposure to advanced machine learning and MLOps technologies. Opportunity to contribute to complex, production-scale AI initiatives and continuously improve ML engineering practices. Supportive environment focused on technical excellence, innovation, collaboration, and measurable business impact.
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