Job

Responsibilities:
• Design, build, and operate end-to-end ML systems, covering data ingestion, training pipelines, model deployment, monitoring, retraining, and inference pipelines (batch/ real-time).
• Design and maintain reproducible model training, evaluation, and experimentation frameworks supporting recommendation systems, search relevance, content intelligence, and GenAI use cases.
• Implement and manage workflow orchestration using Databricks Asset Bundles, Databricks Workflows, Airflow, or equivalent tools.
• Implement CI/CD, model and dataset versioning, release approval checks, and rollback procedures.
• Own ML lifecycle management on Databricks, leveraging the Databricks ecosystem for experimentation, versioning, deployment, and monitoring.
• Develop monitoring solutions for model performance, business KPIs, data quality, drift detection, inference reliability, and operational costs.
• Ensure operational stability, reliability, and observability of ML systems across environments.
• Collaborate closely with Data Scientists to productionize research models and establish scalable, governed ML workflows.
• Enable A/B testing and reliable logging of impressions, recommendations, search results, and user interactions

Skills:
Must have:
• 3+ years of hands-on experience in MLOps / ML Engineering within large-scale, production environments.
• Robust experience with end-to-end ML lifecycle management, from training to deployment, monitoring, retraining and scoring using MLFlow.




• Hands-on experience with Databricks for ML workloads, including model execution, orchestration, and operationalization.
• Strong proficiency in Python, SQL, and PySpark.
• Experience supporting GenAI or LLM workloads operationally (model serving, inference pipelines).
• Experience working with any Cloud Provider, preferably AWS.
• Experience designing and operating workflow orchestration pipelines (Databricks Asset Bundles, Databricks Workflows, Airflow, or similar tools).
• Strong knowledge of CI/CD concepts applied to ML systems.
• Solid understanding of distributed system performance optimization, including compute, storage, and job execution tuning. Good to Have:
• Familiarity with Lakehouse architecture, data governance, and modern data platform practices.
• Exposure to model validation frameworks, drift detection, and ML observability tools.
• Ability to work with global stakeholders in cross-functional, matrixed environments.
• Exposure to search relevance optimization using Elasticsearch/OpenSearch Eligibility:
• Master’s or Bachelor's degree in Math, Statistics, Economics, Computer Science or related analytics field from top-tier universities with strong record of achievement
• 3+ Years of experience (The candidate should have minimum 3+ years of experience in Databricks Unified Data Analytics Platform) Job

Location:
Bengaluru/ Delhi NCR Shift Timing: 1:00PM IST – 10:00 PM IST

Experience:
3+ years

📌 Machine Learning Specialist (Gurugram)
🏢 EXL
📍 Gurugram