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Primary Skills & Responsibilities: 3 positions/ 2-4 Yrs exp/Chennai & Blore location MSP 1. Python Programming: Proficiency in Python for data engineering and machine learning tasks. 2. Spark: Experience with Apache Spark for distributed data processing. 3. Databricks Platform: Familiarity with Databricks notebooks, clusters, jobs, and workspace management 4. MLOPS: Strong experience with DevOps and MLOps on Azure, including Azure Devops CI/CD pipelines, Docker, Kubernetes, and infrastructure automation 5. Graph DB: Experience with graph databases such as Nebula Graph, Tiger Graph and Neo4j for relationship-based modeling, analytics, and ML use cases. Design and build end-to-end knowledge graph architectures from data ingestion and schema/ontology design to graph storage and querying. Design build and maintain end-to-end MLOps pipelines for training, testing, deploying, and monitoring ML models in production. Secondary skills and Responsibilities 1. Strong hands-on experience with machine learning, deep learning, and generative AI 2. Knowledge of cloud services, monitoring tools, logging, and security nest practices for ML systems. 3. Ability to integrate ML/DL models with knowledge graphs for intelligent insights. 4. Collaborate with product, data science, and engineering teams for design scalable, secure, and reliable knowledge graph systems.   Responsibilities Primary Skills & Responsibilities: 3 positions/ 2-4 Yrs exp/Chennai & Blore location MSP 1. Python Programming: Proficiency in Python for data engineering and machine learning tasks. 2. Spark: Experience with Apache Spark for distributed data processing. 3. Databricks Platform: Familiarity with Databricks notebooks, clusters, jobs, and workspace management 4. MLOPS: Strong experience with DevOps and MLOps on Azure, including Azure Devops CI/CD pipelines, Docker, Kubernetes, and infrastructure automation 5. Graph DB: Experience with graph databases such as Nebula Graph, Tiger Graph and Neo4j for relationship-based modeling, analytics, and ML use cases. Design and build end-to-end knowledge graph architectures from data ingestion and schema/ontology design to graph storage and querying. Design build and maintain end-to-end MLOps pipelines for training, testing, deploying, and monitoring ML models in production. Secondary skills and Responsibilities 1. Strong hands-on experience with machine learning, deep learning, and generative AI 2. Knowledge of cloud services, monitoring tools, logging, and security nest practices for ML systems. 3. Ability to integrate ML/DL models with knowledge graphs for intelligent insights. 4. Collaborate with product, data science, and engineering teams for design scalable, secure, and reliable knowledge graph systems.   Qualifications Any Degree