Lead MLOps DevOps Engineer

Sonata Software; Hyderabad, Chennai District, Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Sonata Software is looking for a lead-level MLOps/DevOps leader in Hyderabad to design and operate scalable ML pipelines and cloud-native infrastructure. You will drive automation, governance, and platform reliability across development and production environments, mentoring teams and defining deployment standards for model serving.

The role emphasizes collaboration with data science, security, and engineering teams to accelerate production adoption while ensuring governance and security

Qualifications

• Experience with MLOps lifecycle and model deployment
• Strong knowledge of Kubernetes and containerized deployments
• Proficiency in CI/CD pipelines and release engineering
• Infrastructure as Code and secrets management
• Monitoring and observability practices
• Proficient in Python

Responsibilities

• Design and operate scalable MLOps pipelines for ML workloads
• Lead cloud-native infrastructure, orchestration, and automation across environments
• Establish monitoring, logging, and alerting standards for reliability
• Partner with data science, engineering, and security teams to productionize ML
• Define deployment standards for model serving and environment parity
• Drive best practices, mentor engineers on automation patterns
• Strengthen secrets handling, access controls, and release governance

Skills

MLOps & Model Lifecycle

Cloud-Native Infrastructure & Orches

Delivery Automation & Release Eng.

Infrastructure Provisioning & Security

Monitoring and observability

Python

Tools

Kubernetes

Docker

Terraform

Jenkins

Git

MLflow

Kubeflow

Job description

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

Good-to-Have Skills

• Operating Systems & Scripting: Linux, Bash
• Version Control & Source Management: Git, GitHub Actions
• Cloud Platforms: Cloud platforms, AWS (Amazon Web Services), Microsoft Azure, Google Cloud Platform
• Containerization & Deployment: Docker, Helm, Argo CD
• Infrastructure Automation & Configuration Management: Terraform, Ansible
• CI/CD Platforms: Jenkins, GitLab CI, Azure DevOps
• Cloud Monitoring & Logging: Prometheus, Grafana, ELK stack
• MLOps Platforms & Workflow Orchestration: MLflow, Kubeflow, Airflow, Metaflow, AWS SageMaker
• Data & API Integration: SQL, REST APIs
• Cloud Security & Access Control: IAM and cloud security controls, Service mesh
• TensorFlow