MLOps Manager

Anblicks; Hyderabad; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

Anblicks in Hyderabad is seeking a Staff MLOps Engineer (Manager) to lead the design and scale of MLOps and DevOps platforms. You will mentor a team and ensure the delivery of secure, scalable ML solutions. The ideal candidate will have extensive experience in DevOps and MLOps, proven skills in Infrastructure-as-Code, and hands-on knowledge of CI/CD tools. This role emphasizes operationalizing ML models and driving automation in cloud environments.

Qualifications

• 10+ years of experience in DevOps, MLOps, or Platform Engineering roles.
• 5+ years of people management experience, leading teams of 5+ engineers.
• Strong hands-on expertise in building and scaling MLOps pipelines.

Responsibilities

• Lead and mentor a team of MLOps/DevOps engineers.
• Architect, build, and scale end-to-end MLOps platforms.
• Operationalize ML models into production focusing on performance.

Skills

DevOps

MLOps

Platform Engineering

Infrastructure-as-Code

CI/CD

Python

Docker

Kubernetes

Apache Spark

GitHub Actions

Tools

Terraform

Jenkins

Airflow

Kubeflow

MLflow

Job description

Experience: 12–17 Years

About the Role

We are looking for a Staff MLOps Engineer (Manager) to lead the design, build, and scale of enterprise-grade MLOps and DevOps platforms. This role combines hands-on engineering excellence with team leadership, focusing on productionizing machine learning systems, enabling developer productivity, and driving automation at scale.

You will work at the intersection of ML engineering, cloud infrastructure, and platform engineering, helping teams deliver reliable, scalable, and secure ML solutions in production.

Key Responsibilities

• Lead and mentor a team of MLOps/DevOps engineers, driving technical excellence and delivery outcomes
• Architect, build, and scale end-to-end MLOps platforms and CI/CD pipelines for ML workloads
• Design and implement automated deployment pipelines for training, testing, and model serving at scale
• Operationalize ML models into production with a focus on performance, reliability, and observability
• Partner with data scientists and engineering teams to enable self-service ML platforms and developer tooling
• Implement Infrastructure-as-Code (IaC) and automation frameworks for cloud environments
• Ensure platform compliance with security, governance, and reliability standards
• Troubleshoot complex production issues and continuously improve developer experience and system resilience
• Drive best practices for CI/CD, testing, monitoring, and release management across ML and data platforms
• Evaluate and optimize environments supporting large-scale data pipelines and ML workflows

Required Qualifications

• 10+ years of experience in DevOps, MLOps, or Platform Engineering roles
• 5+ years of people management experience, leading teams of 5+ engineers
• Strong hands-on expertise in building and scaling MLOps pipelines and platforms
• Proven experience with Infrastructure-as-Code (Terraform preferred) in public cloud environments
• Deep experience with CI/CD tools such as GitHub Actions, Jenkins, and code quality/security tools (e.g., Snyk)
• Strong knowledge of MLOps and orchestration frameworks such as Airflow, Kubeflow, MLflow, or similar
• Experience deploying and managing ML models in production at scale
• Hands-on experience with distributed data processing frameworks such as Apache Spark, EMR, or Databricks
• Strong programming skills in Python (preferred) or Node.js/Bash
• Experience with containerization and orchestration (Docker, Kubernetes)
• Strong understanding of cloud platforms (AWS, Azure, or GCP) and cloud-native services
• Experience with data platforms and services such as Snowflake, Redshift, Glue, BigQuery, or similar
• Solid understanding of distributed systems, monitoring, logging, and reliability engineering
• Experience with Git-based workflows and version control best practices

Preferred Qualifications

• Experience with configuration management tools (Ansible, Chef, Puppet)
• Familiarity with ML libraries and frameworks such as scikit-learn, PyTorch, TensorFlow
• Exposure to large-scale inference systems and batch/real-time scoring architectures
• Experience supporting multi-runtime environments (Node.js, Java/Spark/Scala, React)