Senior Systems Engineer - Data DevOps/MLOps
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Salary
≈ $18k – $41k per year (Estimated)
Location
In office (Gurgaon)
Seniority
Senior · 5+ years exp
Employment
Full-Time
First seen by Alion on Oct 26, 2026. EPAM Systems scores D on the Alion truth index.
Overview
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EPAM Systems is an American software engineering and digital transformation company founded in 1993 by Arkadiy Dobkin and Leonid Lozner, and headquartered in Newtown, Pennsylvania. It builds custom software products, platforms and data systems for large enterprises, combining consulting and design with engineering teams distributed across Central and Eastern Europe, India, Latin America and Western Europe. Listed on the New York Stock Exchange and a member of the S&P 500, the company relocated large parts of its delivery capacity after the 2022 invasion of Ukraine and has since centred its offering on cloud modernisation and AI-assisted engineering.
We're seeking a skilled Senior Systems Engineer with deep expertise in Data DevOps/MLOps to join our growing team.
The right candidate will bring strong knowledge of data engineering, pipeline automation, and the operationalization of machine learning models. This position calls for a collaborative professional capable of architecting, implementing, and maintaining large-scale data and ML pipelines that support the company's broader goals.
Responsibilities
The right candidate will bring strong knowledge of data engineering, pipeline automation, and the operationalization of machine learning models. This position calls for a collaborative professional capable of architecting, implementing, and maintaining large-scale data and ML pipelines that support the company's broader goals.
Responsibilities
- Build, launch, and maintain CI/CD pipelines supporting data integration and ML model deployment
- Establish and maintain cloud-based infrastructure for data processing and model training
- 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 system performance and reliability
- Oversee data versioning, lineage tracking, and reproducibility across ML experiments
- Continuously identify opportunities to improve deployment workflows, scalability, and infrastructure resilience
- Enforce robust security measures to protect data integrity and ensure regulatory compliance
- Diagnose and resolve problems across the entire data and ML pipeline lifecycle
- 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 comparable positions
- Strong command of cloud platforms such as Azure, AWS, or GCP
- Experience using Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible
- Skilled in containerization and orchestration tools, including Docker and Kubernetes
- Practical experience with data processing frameworks like Apache Spark and Databricks
- Strong programming skills in Python, along with familiarity in data manipulation and ML libraries such as Pandas, TensorFlow, and PyTorch
- Working knowledge of CI/CD tools, including Jenkins, GitLab CI/CD, and GitHub Actions
- Hands-on experience with version control systems and MLOps platforms such as Git, MLflow, and Kubeflow
- Solid understanding of monitoring, logging, and alerting tools like Prometheus and Grafana
- Strong analytical and problem-solving skills, with the ability to work both independently and collaboratively
- Excellent communication and documentation abilities
- 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
- Relevant certifications in cloud computing or data engineering
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