We're seeking a motivated, detail-oriented Senior Systems Engineer specializing in Data DevOps/MLOps to join our team.

The right candidate will have in-depth knowledge of data engineering, data pipeline automation, and the integration of machine learning models into live environments. This position suits a collaborative individual skilled at creating, launching, and overseeing scalable data and ML pipelines that support strategic goals.

Responsibilities

- Design CI/CD pipelines for data integration and machine learning model deployment

- Deploy and maintain cloud-based infrastructure for data processing and model training

- Automate workflows such as data validation, transformation, and orchestration

- Collaborate with data scientists, software engineers, and product teams to bring ML models into production

- Improve performance and reliability through optimized model serving and monitoring

- Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments

- Pinpoint opportunities to strengthen deployment processes, scalability, and infrastructure resilience

- Apply security protocols to protect data integrity and ensure compliance

- 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 discipline

- At least 5 years of experience in Data DevOps, MLOps, or similar roles

- Hands-on experience with cloud platforms such as Azure, AWS, or GCP

- Working knowledge of Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible

- Strong command of containerization and orchestration tools such as Docker and Kubernetes

- Experience with data processing frameworks like Apache Spark or Databricks

- Solid Python skills, including familiarity with data and ML libraries such as Pandas, TensorFlow, or PyTorch

- Exposure to CI/CD tools like Jenkins, GitLab CI/CD, or GitHub Actions

- Understanding of version control systems like Git and MLOps platforms such as MLflow or Kubeflow

- Familiarity with monitoring, logging, and alerting tools like Prometheus or Grafana

- Sharp problem-solving skills with the ability to thrive independently or as part of a team

- Robust communication and documentation abilities

Nice to have

- Exposure to DataOps practices and tools such as Airflow or dbt

- Awareness of data governance frameworks and tools like Collibra

- Familiarity with Big Data technologies such as Hadoop or Hive

- Certifications in cloud platforms or data engineering disciplines

📌 Senior Systems Engineer - Data DevOps/MLOps (Coimbatore)
🏢 EPAM Systems
📍 Coimbatore