Senior MLOps Engineer

Confidential Client; Bengaluru; On-site; INR 4,200,000 - 7,000,000; Full time

Job summary

Confidential Client in Bengaluru, India, seeks a Senior MLOps Engineer with 8–12 years of experience to lead enterprise-scale ML infrastructure initiatives.

You will design and implement CI/CD driven pipelines, scalable model serving, and robust monitoring across AWS, Azure, and GCP, while ensuring security, governance, and auditability of deployments. You will mentor engineers and collaborate with cross-functional teams.

Qualifications

• 8–12 years of experience in designing enterprise-scale ML infrastructure and operational frameworks
• Deep expertise in CI/CD automation and cloud-native ML deployment
• Experience with observability, governance, and scalable AI platform engineering
• Knowledge of security and compliance in AI deployments
• Leadership and stakeholder management skills
• Exposure to LLMOps and Generative AI deployment
• Familiarity with SageMaker, Vertex AI, and Azure ML
• Experience with Spark/PySpark and real-time ML systems
• Experience with GPU infrastructure management
• BFSI, Healthcare, or regulated industry exposure
• Strong programming in Python and cloud platforms (AWS, Azure, GCP)
• Experience with model deployment, monitoring, and drift detection

Responsibilities

• Architect and implement enterprise-grade MLOps platforms and deployment frameworks
• Lead automation of end-to-end ML lifecycle management
• Define CI/CD and governance standards for AI/ML systems
• Design scalable infrastructure for model serving and inference
• Implement advanced monitoring frameworks for model health, drift, latency, and reliability
• Collaborate with Data Science, Security, and Platform Engineering teams
• Optimize cloud infrastructure and ML workloads for scalability and performance
• Mentor junior engineers and establish operational best practices
• Ensure compliance, auditability, and security of AI deployments
• Drive adoption of modern MLOps and LLMOps practices

Skills

Python

Kubernetes

Docker

Terraform

Linux

CI/CD

Leadership

Stakeholder management

LLMOps

Generative AI deployment

Tools

Jenkins

GitHub Actions

GitLab CI/CD

MLflow

Kubeflow

Airflow

Model Registry

SageMaker

Vertex AI

Azure ML

Spark PySpark

Kafka

GPU infrastructure

Vector Databases

Feature Stores

Job description

Senior MLOps Engineer

We are seeking a Senior MLOps Engineer with 8–12 years of experience in designing enterprise-scale ML infrastructure and operational frameworks. The ideal candidate will have deep expertise in CI/CD automation, cloud-native ML deployment, observability, governance, and scalable AI platform engineering. You will lead MLOps initiatives, establish best practices, and drive automation strategies for secure and compliant AI model lifecycle management.

Key Responsibilities

• Architect and implement enterprise-grade MLOps platforms and deployment frameworks.
• Lead automation of end-to-end ML lifecycle management.
• Define CI/CD and governance standards for AI/ML systems.
• Design scalable infrastructure for model serving and inference.
• Implement advanced monitoring frameworks for model health, drift, latency, and reliability.
• Collaborate with Data Science, Security, and Platform Engineering teams.
• Optimize cloud infrastructure and ML workloads for scalability and performance.
• Mentor junior engineers and establish operational best practices.
• Ensure compliance, auditability, and security of AI deployments.
• Drive adoption of modern MLOps and LLMOps practices.

Requirements

• Strong expertise in Python, Kubernetes, Docker, Terraform, Linux.
• Advanced experience with CI/CD pipelines, Jenkins, GitHub Actions, GitLab CI/CD.
• Experience with MLflow, Kubeflow, Airflow, Model Registry.
• Strong cloud expertise in AWS, Azure, GCP.
• Experience in Model Deployment, Model Monitoring, Drift Detection, Infrastructure Automation.
• Knowledge of Security and compliance frameworks, DevSecOps, Infrastructure as Code.
• Strong leadership and stakeholder management skills.
• Preferred experience with LLMOps, Generative AI deployment, Vector Databases, Feature Stores.
• Expertise in SageMaker, Vertex AI, Azure ML.
• Familiarity with Spark/PySpark, Kafka, Real-time ML systems.
• Exposure to GPU infrastructure management, Distributed model serving.
• Knowledge of Responsible AI and governance frameworks.
• Experience in BFSI, Healthcare, or highly regulated industries.

Work Arrangements

• Full-time position located in Bengaluru, Karnataka, India.
• Remote work details not specified.