Lead MLOps DevOps Engineer | Bangalore | 8-12 Years

Sonata Software; Hyderabad, Chennai District, Bengaluru; Hybrid; INR 4,000,000 - 6,500,000; Full time

Job summary

Sonata Software is seeking a Lead MLOps DevOps Engineer to own the design and operation of ML platforms. The role focuses on cloud-native infrastructure, container orchestration, automation, and production model serving to enhance delivery speed, governance, and stability across ML environments.

You will collaborate with data science, engineering, and security teams to productionize workloads with governance, repeatability, and compliance, establishing deployment standards and enabling

Qualifications

• MLOps experience with end-to-end model lifecycle

Responsibilities

• Design and operate scalable MLOps pipelines for build, test, deployment, and release of ML workloads.
• Lead cloud-native infrastructure, container orchestration, and automation across dev and prod to improve scalability.
• Establish monitoring, logging, and alerting standards for model and platform services.
• Partner with data science, engineering, and security teams to productionize ML workloads with governance and compliance.
• Define deployment standards for model serving and environment parity to reduce release risk.
• Mentor engineers on platform automation patterns and improve delivery speed and operational efficiency.
• Strengthen secrets handling, access controls, and release governance across CI/CD and runtime environments.

Skills

MLOps

Model deployment

Containers

Kubernetes

CI/CD

Infrastructure as Code

Secrets management

Monitoring

Python

Data APIs

Tools

GitLab

GitHub

AWS

Docker

Terraform

Ansible

CloudFormation

DataDog

Dynatrace

MLflow

Airflow

Job description

Job Title: Lead MLOps DevOps Engineer

Work Location: Bangalore, India

Experience Range: 8-12 Years

What We're Looking For

This lead-level role owns the design and operation of MLOps and DevOps platforms that support reliable model build, test, deployment, and release workflows. The position combines cloud-native infrastructure, container orchestration, automation, and production model serving to improve delivery speed, governance, and operational stability across machine learning environments.

Key Responsibilities

• Design and operate scalable MLOps pipelines that support reliable build, test, deployment, and release processes for machine learning workloads.
• Lead the implementation of cloud-native infrastructure, container orchestration, and automation practices across development and production environments to improve scalability and repeatability.
• Establish monitoring, logging, and alerting standards for model and platform services to improve reliability, performance, and incident response.
• Partner with data science, engineering, and security teams to productionize machine learning workloads with governance, repeatability, and compliance.
• Define deployment standards for model serving, artifact promotion, and environment parity to reduce release risk and accelerate production adoption.
• Drive technical best practices, mentor engineers on platform automation patterns, and continuously improve delivery speed and operational efficiency.
• Strengthen secrets handling, access controls, and release governance across CI/CD and runtime environments to improve security and audit readiness.

Must-Have Skills

• MLOps & Model Lifecycle: MLOps, Model deployment and serving
• Cloud-Native Infrastructure & Orchestration: Containers, Kubernetes
• Delivery Automation & Release Engineering: CI/CD pipelines, Artifact and package management
• Infrastructure Provisioning & Security: Infrastructure as Code, Secrets management
• Monitoring and observability
• Python

Technical Skills

• Operating Systems & Scripting: Linux, Bash and Python
• Version Control & Source Management: Gitlab, GitHub
• Cloud Platforms: AWS (Amazon Web Services)
• Containerization & Deployment: Docker, Kubernetes
• Infrastructure Automation & Configuration Management: Terraform, Ansible, CloudFormation
• CI/CD Platforms: GitLab CI/CD
• Observability Tools: DataDog or Dynatrace
• MLOps Platforms & Workflow Orchestration: MLflow, Airflow, AWS SageMaker
• Data & API Integration: SQL, REST APIs
• Cloud Security & Access Control: IAM and cloud security controls

Why Join This Opportunity?

• Own the platform layer that turns machine learning models into reliable production services.
• Influence DevOps and MLOps standards across build, deployment, observability, and governance workflows.
• Work at lead level on cloud-native automation and model-serving patterns that improve release speed and operational stability.