Back to SearchSenior Systems Engineer - Data DevOps/MLOps
Office in India: Chennai, & 5 others
Data DevOps& 19 others
Looking for something else?
Find a vacancy that works for you. Send us your CV to receive a personalized offer.
Find me a job
Choose an option
Our team is seeking a skilled and committed Senior Systems Engineer who brings deep expertise in Data DevOps/MLOps to strengthen our organization.
The successful applicant will demonstrate a thorough understanding of data engineering practices, automated data pipelines, and the operational deployment of machine learning models. This position requires a collaborative individual capable of architecting, implementing, and overseeing large-scale data and ML pipelines that support our company's goals.
Responsibilities
- Build, launch, and oversee CI/CD pipelines supporting data integration and ML model rollout
- Establish and maintain cloud-based infrastructure for data processing and model training operations
- Streamline data validation, transformation, and workflow orchestration through automation
- Partner with data scientists, software engineers, and product teams to ensure seamless ML model integration into production environments
- Improve model serving and monitoring capabilities to increase performance and reliability
- Oversee data versioning, lineage tracking, and ensure ML experiments remain reproducible
- Continuously identify opportunities to improve deployment workflows, scalability, and infrastructure durability
- Enforce robust security measures that protect data integrity and meet regulatory standards
- Diagnose and resolve problems across the entire data and ML pipeline lifecycle
Requirements
- Bachelor's or Master's degree in Computer Science, Data Engineering, or related discipline
- Minimum 5 years of relevant experience in Data DevOps, MLOps, or comparable positions
- Skilled in cloud platforms such as Azure, AWS, or GCP
- Experience working with Infrastructure as Code tools like Terraform, CloudFormation, or Ansible
- Strong command of containerization and orchestration tools such as Docker and Kubernetes
- Practical experience using data processing frameworks like Apache Spark and Databricks
- Programming skills in Python, along with familiarity with data manipulation and ML libraries such as Pandas, TensorFlow, and PyTorch
- Knowledge of CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions
- Hands-on experience with version control systems and MLOps platforms including Git, MLflow, and Kubeflow
- Solid grasp of monitoring, logging, and alerting tools such as Prometheus and Grafana
- Strong analytical and problem-solving capabilities, with the ability to perform well both independently and collaboratively
- Effective communication and documentation skills
Nice to have
- Experience with DataOps principles and tools such as Airflow and dbt
- Understanding of data governance platforms like Collibra
- Exposure to Big Data technologies such as Hadoop and Hive
- Cloud platform or data engineering certifications