Lead Data DevOps - MLOps Expertise

Data DevOps, MLOps, MLflow, Kubeflow, CI/CD, Infrastructure, Docker, Kubernetes, Apache Spark, Databricks, Python, Pandas, TensorFlow, PyTorch, Jenkins, Git, GitHub Actions, Terraform Cloud, Ansible, Data Analysis

Hyderabad, Bangalore, Pune, Chennai, Gurgaon

We are seeking a skilled and driven Lead Data DevOps Engineer with strong MLOps expertise to join our team.

The ideal candidate will have a deep understanding of data engineering, automation in data pipelines, and operationalizing machine learning models. The role requires a collaborative professional capable of designing, deploying, and managing scalable data and ML pipelines that align with business goals.

Responsibilities

Develop, deploy, and manage CI/CD pipelines for data integration and machine learning model deployment
Implement and maintain infrastructure for data processing and model training using cloud-based tools and services
Automate data validation, transformation, and workflow orchestration processes
Collaborate with data scientists, software engineers, and product teams to ensure seamless integration of ML models into production
Optimize model serving and monitoring to enhance performance and reliability
Maintain data versioning, lineage tracking, and reproducibility of ML experiments
Proactively identify opportunities to improve deployment processes, scalability, and infrastructure resilience
Ensure robust security measures are in place to protect data integrity and compliance with regulations
Troubleshoot and resolve issues across the data and ML pipeline lifecycle

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
8+ years of experience in Data DevOps, MLOps, or related roles
Strong proficiency in cloud platforms such as Azure, AWS, or GCP
Experience with Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible
Expertise in containerization and orchestration technologies (e.g., Docker, Kubernetes)
Hands-on experience with data processing frameworks (e.g., Apache Spark, Databricks)
Proficiency in programming languages such as Python, with knowledge of data manipulation and ML libraries (e.g., Pandas, TensorFlow, PyTorch)
Familiarity with CI/CD tools (e.g., Jenkins, GitLab CI/CD, GitHub Actions)
Experience with version control tools (e.g., Git) and MLOps platforms (e.g., MLflow, Kubeflow)
Strong understanding of monitoring, logging, and alerting systems (e.g., Prometheus, Grafana)
Excellent problem-solving skills and the ability to work independently and as part of a team
Strong communication and documentation skills

Nice to have

Experience with DataOps concepts and tools (e.g., Airflow, dbt)
Knowledge of data governance and tools like Collibra
Familiarity with Big Data technologies (e.g., Hadoop, Hive)
Certifications in cloud platforms or data engineering