Senior ML Operations Engineer

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
• Strong 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-time inference and data processing.
• Strong proficiency in Python programming language.
• Understanding of model monitoring, data drift detection, model explainability, and AI governance.
• Strong problem-solving skills and the ability to iterate and experiment to optimize AI model behavior.
• Strong analytical, problem-solving, and stakeholder communication skills.

Preferred Qualifications

• Experience with Generative AI, LLMs, Agentic AI, and RAG-based applications.
• Experience with Databricks Lakehouse, MLflow, Unity Catalog, and Delta Lake.
• Relevant certifications in Cloud, Machine Learning, Data Engineering, or MLOps.

• Same as above
• Bachelor's/Master's in Engineering 5-8 years