Job Role: Staff MLOps Engineer (Manager)

Experience: 12–17 Years

Location: Hyderabad, India

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)
Cloud certifications (AWS/GCP/Azure)

MLOps Manager