Description
Key Responsibilities
· Design, develop, and deploy machine learning models for AI-driven business solutions.
· Build and maintain scalable ML pipelines covering data ingestion, feature engineering, model training, validation, deployment, and monitoring.
· Implement MLOps best practices including experiment tracking, model versioning, CI/CD, model governance, and automated retraining.
· Collaborate with Data Scientists and Data Engineers to operationalize machine learning solutions and accelerate model deployment.
· Develop and optimize distributed data processing workflows using Spark/PySpark and cloud-native technologies.
· Monitor model performance, data drift, and infrastructure health, ensuring reliability and scalability in production.
· Build Endpoints and inference services for real-time and batch scoring applications.
· Implement automated testing, validation, and deployment pipelines for ML workloads.
· Develop and deploy GenAI applications leveraging LLMs, RAG frameworks, vector databases, and prompt engineering.
· Work closely with DevOps teams to optimize cloud infrastructure, security, scalability, and deployment processes.
· Maintain technical documentation, architectural designs, and operational runbooks.
Required Qualifications
· 5+ years of experience in Machine Learning Engineering, Data Science, MLOps, or Data Engineering.
· Experience with MLOps platforms such as MLflow, Azure ML, Databricks
· Robust knowledge of CI/CD pipelines, Git/GitHub, containerization (Docker), and orchestration platforms (Kubernetes).
· Exposure in deploying a use case in production leveraging Generative AI involving prompt engineering and RAG Framework
· Experience with Spark/PySpark and distributed data processing frameworks.
· Hands-on experience deploying and managing machine learning models in production environments.
· Experience working with Azure, AWS, or GCP cloud ecosystems.
· Exposure to Kafka or streaming frameworks for real-tim
📌 Senior ML Operations Engineer (Bengaluru)
🏢 EXL
📍 Bengaluru