4604092-Senior Manager

EXL; Gurugram District; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

EXL in Gurugram, India seeks an Analytics & Data Engineering expert specializing in ML, MLOps, and DevOps to design scalable pipelines and production-grade ML systems. The role blends hands-on coding with solution design and requires close collaboration with data scientists, product teams, and engineering stakeholders.

Key tasks include building ETL/ELT processes, implementing MLOps frameworks for model lifecycle, CI/CD, containerization, and cloud deployment across AWS, GCP, or Azure.

Qualifications

• 6+ years in analytics and data engineering roles.
• Experience deploying ML models in production and maintaining ML pipelines.
• Hands-on with MCP/OpenAPI integrations and agent-based architectures.

Responsibilities

• Design, build, and maintain scalable data pipelines for analytics and ML workloads.
• Implement MLOps frameworks to manage model lifecycle and monitoring.
• Apply DevOps best practices to ML and data workflows.
• Develop data models, feature stores, and ML serving architectures.
• Collaborate with data scientists and engineers to productionize models.

Skills

SQL

Python

Airflow

Databricks

Spark

MLOps

Docker

Kubernetes

OpenAPI

A2A protocols

Education

Master's degree in Computer Science/Data Engineering

Tools

Airflow

Prefect

MLflow

Kubeflow

Jenkins

GitHub Actions

GitLab CI

Docker

Kubernetes

AWS

GCP

Azure

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.