Manager

• Design, build, and maintain scalable data pipelines and ETL/ELT processes for analytics and ML workloads.
• Implement MLOps frameworks to manage model lifecycle (training, deployment, monitoring, and retraining).
• Apply DevOps best practices (CI/CD, containerization, infrastructure as code) to ML and data engineering workflows.
• Develop and optimize data models, feature stores, and ML serving architectures.
• Collaborate with AI/ML teams to integrate models into production environments.
• Support agent development using MCP/OpenAPI to MCP wrapper and A2A (Agent-to-Agent) communication protocols.
• Ensure data quality, governance, and compliance with security best practices.
• Troubleshoot and optimize data workflows for performance and reliability.

• Core:

• 6+ years in analytics and data engineering roles.
• Proficiency in SQL, Python, and data pipeline orchestration tools (e.g., Airflow, Prefect).
• Experience with distributed data processing frameworks (e.g., Spark, Databricks).
• ML/MLOps:

• Experience deploying and maintaining ML models in production.
• Knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.).
• DevOps:

• Hands-on experience with CI/CD (Jenkins, GitHub Actions, GitLab CI).
• Proficiency with Docker, Kubernetes, and cloud-based deployment (AWS, Azure, GCP).
• Specialized:

• Experience with MCP/OpenAPI to MCP wrapper integrations.
• Experience working with A2A protocols in agent development.
• Familiarity with agent-based architectures and multi-agent communication patterns.

• Master’s degree in Computer Science, Data Engineering, or related field.
• Experience in real-time analytics and streaming data pipelines (Kafka, Kinesis, Pub/Sub).
• Exposure to LLM-based systems or intelligent agents.
• Strong problem-solving skills and ability to work in cross-functional teams.

EXL Service