MLOps Engineer - NCR/Pune/Bangalore

EXL; Bengaluru; On-site; INR 3,000,000 - 5,500,000; Full time

Job summary

EXL is seeking an experienced Machine Learning Engineer with strong MLOps expertise to design, deploy, and manage scalable AI/ML solutions in production at Bengaluru.

You will collaborate with Data Scientists and business stakeholders across the end-to-end lifecycle, implement CI/CD pipelines, monitor drift, and develop GenAI applications using LLMs, RAG, and cloud services.

Qualifications

• 4+ years of experience in ML engineering, data science, MLOps, or Data Engineering.
• Hands‑on with end-to-end ML lifecycle, from data prep to monitoring.
• Experience deploying models in production using CI/CD and cloud platforms.

Responsibilities

• Design, develop, and deploy ML models for AI‑driven business solutions.
• Build scalable ML pipelines covering ingestion, feature engineering, model training, validation, deployment, and monitoring.
• Implement MLOps practices including experiment tracking, versioning, CI/CD, governance, and retraining.
• Collaborate with Data Scientists/Engineers to operationalize ML solutions.
• Develop and optimize distributed data processing workflows using Spark/PySpark and cloud‑native tech.
• Monitor model performance and data drift in production; ensure reliability.
• Build endpoints and inference services for real‑time and batch scoring.
• Develop automated testing, validation, and deployment pipelines for ML workloads.
• Develop and deploy GenAI applications leveraging LLMs and RAG frameworks.
• Work with DevOps to optimize cloud infrastructure, security, and deployment.

Skills

Machine Learning

MLOps

Python

Spark/PySpark

CI/CD

Docker

Kubernetes

Cloud platforms

LLMs / Generative AI

Data Engineering basics

Education

Bachelor’s or Master’s degree in CS/Engineering

Tools

MLflow

Azure ML

Databricks

Git/GitHub

Job description

We are looking for an experienced Machine Learning Engineer with strong MLOps expertise to design, develop, deploy, and manage scalable AI/ML solutions in production environments. The ideal candidate should possess hands‑on experience across the end‑to‑end machine learning lifecycle, including data preparation, model development, deployment, monitoring, automation, and continuous improvement. The role requires close collaboration with Data Scientists, and business stakeholders to build reliable and production‑grade ML platforms.

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

• 4+ 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.

Education

• Bachelor’s or master’s degree in computer science, Engineering, or a related field.