Job Overview

EPAM is looking for a Senior Systems Engineer with a strong focus on Data DevOps and MLOps for its team in Coimbatore, India. This is an experienced engineering opportunity for professionals who can connect data engineering, cloud infrastructure, automation, and machine learning operations to create dependable production environments.

The role focuses on building and managing scalable data and machine learning pipelines. You will work across the lifecycle of data processing and ML model delivery, from automated validation and transformation to deployment, monitoring, troubleshooting, and continuous improvement. The position requires a practical understanding of modern cloud platforms, Infrastructure as Code, containers, CI/CD, data processing frameworks, and MLOps practices.

The successful candidate will collaborate with data scientists, software engineers, and product teams to move machine learning solutions into operational environments. Attention to security, reproducibility, data lineage, reliability, and infrastructure resilience is important throughout the development and deployment process.

Key Responsibilities

• Design and maintain CI/CD pipelines that support data integration workflows and machine learning model deployment.
• Build and manage cloud infrastructure used for data processing, model training, and production workloads.
• Automate data validation, transformation, workflow execution, and other repetitive activities across the data and ML lifecycle.
• Work with data scientists and software engineering teams to integrate machine learning models into production systems.
• Improve model-serving performance, pipeline reliability, and monitoring capabilities.
• Establish practices for data versioning, lineage, experiment reproducibility, and consistent ML delivery.
• Identify opportunities to make deployment processes more scalable, resilient, and efficient.
• Apply appropriate security controls to protect data integrity and support compliance requirements.
• Investigate and resolve failures across data pipelines, infrastructure, deployment workflows, and ML operations.
• Contribute to engineering practices that improve the reliability and maintainability of production data and machine learning platforms.

Required Skills

Candidates should have strong experience in Data DevOps, MLOps, or a closely related engineering discipline. The role requires practical cloud experience and the ability to automate infrastructure and deployment processes.

Strong knowledge of Infrastructure as Code is expected, with experience using technologies such as Terraform, CloudFormation, or Ansible. Candidates should also be comfortable working with containerized environments and orchestration platforms such as Docker and Kubernetes.

Python is an important programming skill for this position. Familiarity with data manipulation and machine learning libraries such as Pandas, TensorFlow, and PyTorch is relevant to the role. Experience with data processing technologies such as Apache Spark or Databricks is also required.

Required Technologies

The primary technology environment includes:

• Azure
• AWS
• GCP
• Terraform
• CloudFormation
• Ansible
• Docker
• Kubernetes
• Apache Spark
• Databricks
• Python
• Pandas
• TensorFlow
• PyTorch
• Jenkins
• GitLab CI/CD
• GitHub Actions
• Git
• MLflow
• Kubeflow
• Prometheus
• Grafana

The role also involves monitoring, logging, alerting, CI/CD automation, cloud infrastructure, data pipelines, machine learning model deployment, and MLOps platform practices.

Experience

A minimum of 5 years of professional experience in Data DevOps, MLOps, or related professions is required. Candidates should have hands-on experience supporting data and ML pipelines and should understand how to take machine learning workloads from development environments into production.

Experience with cloud platforms such as Azure, AWS, or GCP is expected. Candidates should also demonstrate experience with Infrastructure as Code, containers, orchestration, CI/CD, data processing, version control, and MLOps tooling.

Education

Applicants should hold a Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

Preferred Skills

Knowledge of DataOps practices can be beneficial, particularly experience with Apache Airflow or dbt. Familiarity with data governance frameworks and tools such as Collibra is also useful.

Additional knowledge of Big Data technologies including Hadoop or Hive is preferred. Cloud or data engineering credentials can further strengthen a candidate’s profile.

Experience with MLflow or Kubeflow, together with monitoring platforms such as Prometheus or Grafana, is relevant to building and operating reliable ML environments.

Soft Skills

Strong problem-solving ability is important because the role involves diagnosing pipeline and infrastructure issues and finding practical solutions. Candidates should be capable of working independently while also collaborating effectively with data scientists, developers, product teams, and other stakeholders.

Clear communication and documentation skills are expected. The ability to explain technical decisions, document operational processes, and coordinate work across teams helps ensure that data and ML solutions remain maintainable and reliable.

Benefits of Working in this Role

The job posting describes opportunities to work on technical challenges with impact across geographies and access to professional development resources. These include an online university, global knowledge-sharing opportunities, external certification learning, sponsored Tech Talks and Hackathons, and access to LinkedIn Learning solutions.

EPAM also describes opportunities to share ideas on international platforms and the possibility of relocating to another EPAM office for short- or long-term projects. The stated benefit package includes health benefits, retirement benefits, paid time off, and flexible benefits. The organization also provides forums for interests beyond work, including CSR, photography, painting, and sports.

Work Mode

The supplied vacancy identifies an office location in Coimbatore. The source does not explicitly state a remote or hybrid work model, so this opportunity is treated as onsite based on the available job information.

Location

This Senior Systems Engineer - Data DevOps/MLOps opportunity is based in Coimbatore, Tamil Nadu, India.

Who Should Apply

This role is suitable for experienced Data DevOps and MLOps professionals who have worked with cloud infrastructure, automated data pipelines, machine learning deployment, and production operations.

It is particularly relevant for engineers who enjoy solving infrastructure and deployment challenges and who want to work across data engineering and machine learning operations. Candidates with strong Python skills, cloud expertise, Kubernetes and Docker experience, CI/CD knowledge, and familiarity with MLOps platforms should consider applying.

Professionals who have additionally worked with Airflow, dbt, Collibra, Hadoop, Hive, or cloud and data engineering certifications can highlight those skills in their applications.

Career Growth

This position offers exposure to multiple areas of modern engineering, including cloud infrastructure, MLOps, DataOps, machine learning deployment, automation, observability, security, and scalable data processing. Working with data scientists, software engineers, and product teams can also broaden technical and cross-functional experience.

The role can help experienced engineers deepen their ability to design reliable ML delivery platforms and develop stronger expertise in production machine learning operations.

Application Advice

When applying, clearly highlight your professional experience in Data DevOps or MLOps and provide specific examples of production systems you have supported or improved. Emphasize your hands-on work with cloud platforms, Infrastructure as Code, containers, CI/CD, data processing, model deployment, monitoring, and troubleshooting.

Mention the tools you have used, such as Terraform, Ansible, Docker, Kubernetes, Python, Spark, Databricks, Jenkins, GitHub Actions, GitLab CI/CD, MLflow, Kubeflow, Prometheus, and Grafana. If you have experience with DataOps, data governance, Big Data, or relevant certifications, include those details as well.

Focus on measurable technical contributions where possible, such as improving pipeline reliability, reducing deployment effort, strengthening monitoring, automating operational tasks, or improving scalability. Keep your application accurate and aligned with the experience and skills you can demonstrate.