ML OPS Engineer

Job location: Gurugram, Bengaluru Overall experience: 4 to 9 years Employment type: Full Time, Permanent Job type: fulltime Role Job Location: Bengaluru/ Delhi NCR Shift Timing: 1:00PM IST 10:00 PM IST Experience: 3+ years EXL (NASDAQ:EXLS) is a leading operations management and analytics company that helps businesses enhance growth and profitability in the face of relentless competition and continuous disruption. Using our proprietary, award-winning Business EXLerator Framework™, which integrates analytics, automation, benchmarking, BPO, consulting, industry best practices and technology platforms, we look deeper to help companies improve global operations, enhance data-driven insights, increase customer satisfaction, and manage risk and compliance. EXL serves the insurance, healthcare, banking and financial services, utilities, travel, transportation and logistics industries. Headquartered in New York, New York, EXL has more than 24,000 professionals in locations throughout the United States, Europe, Asia (primarily India and Philippines), Latin America, Australia and South Africa. EXL Analytics provides data-driven, action-oriented solutions to business problems through statistical data mining, cutting edge analytics techniques and a consultative approach. Leveraging proprietary methodology and best-of-breed technology, EXL Analytics takes an industry-specific approach to transform our clients’ decision making and embed analytics more deeply into their business processes. Our global footprint of nearly 2,000 data scientists and analysts assist client organizations with complex risk minimization methods, advanced marketing, pricing and CRM strategies, internal cost analysis, and cost and resource optimization within the organization. EXL Analytics serves the insurance, healthcare, banking, capital markets, utilities, retail and e-commerce, travel, transportation and logistics industries. Please visit www.exlservice.com for more information about EXL Analytics. 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. Strong 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) & responsibilities Preferred candidate profile