Senior ML Engineer with Databricks Expertise

4 days ago

Bengaluru, Karnataka, India LTIMindtree Full time
Job Description:

LTIMindtree is seeking a Senior ML Engineer to operationalize machine learning workflows driving dynamic pricing and personalized consumer experiences. This position focuses on building robust ML infrastructure, frameworks, and orchestration pipelines.

Key Responsibilities:

  • Develop scalable ML infrastructure on Databricks leveraging Unity Catalog and feature stores for model development and deployment.
  • Design and implement drift detection frameworks ensuring continuous monitoring and high reliability of ML models in production.
  • Develop model calibration frameworks and establish versioning practices for transparency and reproducibility across the ML lifecycle.
  • Optimize reinforcement learning orchestration pipelines including Contextual Bandits for real-time execution in low-latency environments.
  • Create automated frameworks for training, retraining, and validating ML models enabling efficient experimentation and deployment.
  • Implement CICD best practices to streamline the deployment and monitoring of ML models integrating with Databricks workflows and Git-based version control systems.
  • Collaborate with ML Scientists to ship, deploy, and maintain models.
  • Build tools for model performance monitoring, operational analytics, and drift mitigation ensuring reliable operation in production environments.

Qualifications:

  • 7 years of experience in MLOps, ML Engineering, or related roles focusing on deploying and managing ML workflows in production environments.
  • Hands-on experience building drift detection systems, model calibration frameworks, and robust monitoring tools for ML pipelines.
  • Proficient in using Databricks, Apache Spark, ML Flow, Unity Catalog, and feature stores.
  • Expertise in deploying and orchestrating low-latency ML models including reinforcement learning solutions like Contextual Bandits and Q-learning.
  • Experience designing automated training pipelines for ML models focusing on efficiency.
  • Strong knowledge of Git workflows, CICD practices, and tools like GitLab or similar.
  • Proficiency in Python, SQL, and big data processing tools like Spark.
  • Familiarity with ML lifecycle tools such as MLflow, Kubeflow, and Airflow.
  • Strong understanding of model performance monitoring, drift detection, and retraining workflows.

Estimated Salary: ₹25,00,000 - ₹40,00,000 per annum, depending on location and experience.

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