4604091-Manager

EXL Talent Acquisition Team; Gurugram District; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

EXL Talent Acquisition Team seeks a highly skilled Analytics & Data Engineering professional with a strong background in ML, MLOps, and DevOps. The role focuses on scalable data pipelines and production-grade ML systems, collaborating with data scientists, product teams, and engineering stakeholders.

The ideal candidate will design, implement, and maintain end-to-end data workflows, ensure data quality and security, and work on agent development using MCP/OpenAPI and A2A protocols.

Qualifications

• 5+ years in analytics and data engineering roles.
• Experience with SQL, Python, and data pipeline orchestration tools like Airflow or Prefect.
• Experience with distributed data processing (Spark, Databricks).
• Experience deploying and maintaining ML models in production (MLOps).
• Knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI).
• DevOps practices with CI/CD (Jenkins, GitHub Actions, GitLab CI).
• Proficiency with containerization and cloud deployment (Docker, Kubernetes, AWS/Azure/GCP).
• Experience with MCP/OpenAPI to MCP wrapper integrations and A2A protocols.

Responsibilities

• Design, build, and maintain scalable data pipelines for analytics and ML workloads.
• Implement MLOps frameworks to manage model lifecycle including training, deployment, monitoring, and retraining.
• Apply DevOps best practices (CI/CD, containerization, IaC) to ML and data workflows.
• Develop and optimize data models, feature stores, and ML serving architectures.
• Collaborate with AI/ML teams to productionize models.
• Support agent development using MCP/OpenAPI and A2A protocols.
• Ensure data quality, governance, and security compliance.
• Troubleshoot and optimize data workflows for performance and reliability.

Skills

SQL

Python

Airflow

Prefect

Spark

Databricks

MLOps

CI/CD

Docker

Kubernetes

Cloud deployment

Education

Master's degree in Computer Science or related field

Tools

MLflow

Kubeflow

SageMaker

Vertex AI

Jenkins

GitHub Actions

GitLab CI

Docker

Kubernetes

Job description

MLOpsWe are looking for a highly skilled Analytics & Data Engineering professional with a strong background in Machine Learning, MLOps, and DevOps. The ideal candidate will have experience designing and implementing scalable data and analytics pipelines, enabling production-grade ML systems, and supporting agent-based development leveraging MCP/OpenAPI to MCP wrapper and A2A protocols. This role combines hands-on technical work with solution design, and will require close collaboration with data scientists, product teams, and engineering stakeholders.

Key Responsibilities

• 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.

Required Skills & Experience

• 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.

Preferred Qualifications

• 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.