Senior Data DevOps - MLOps Expertise Coimbatore, India
Senior Data DevOps - MLOps Expertise Description
EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.
We are looking for a dedicated and proficient Senior Data DevOps Engineer with extensive MLOps knowledge to enhance our team.
The ideal candidate should possess a comprehensive knowledge of data engineering, data pipeline automation, and machine learning model operationalization. The role demands a cooperative professional skilled in designing, deploying, and managing extensive data and ML pipelines in alignment with organizational objectives.
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Responsibilities
- Develop, deploy, and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for data integration and machine learning model deployment
- Set up and sustain infrastructure for data processing and model training through cloud-based resources and services
- Automate processes for data validation, transformation, and workflow orchestration
- Work closely with data scientists, software engineers, and product teams for a smooth integration of ML models into production
- Enhance model serving and monitoring to boost performance and dependability
- Manage data versioning, lineage tracking, and the reproducibility of ML experiments
- Actively search for enhancements in deployment processes, scalability, and infrastructure resilience
- Implement stringent security protocols to safeguard data integrity and compliance with regulations
- Troubleshoot and solve issues throughout the data and ML pipeline lifecycle
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
- 4+ years of experience in Data DevOps, MLOps, or similar roles
- Proficiency in cloud platforms such as Azure, AWS, or GCP
- Background in Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible
- Expertise in containerization and orchestration technologies including Docker and Kubernetes
- Hands-on experience with data processing frameworks such as Apache Spark and Databricks
- Proficiency in programming languages including Python with an understanding of data manipulation and ML libraries like Pandas, TensorFlow, and PyTorch
- Familiarity with CI/CD tools including Jenkins, GitLab CI/CD, and GitHub Actions
- Experience with version control tools and MLOps platforms such as Git, MLflow, and Kubeflow
- Strong understanding of monitoring, logging, and alerting systems including Prometheus and Grafana
- Excellent problem-solving abilities with capability to work independently and in teams
- Strong skills in communication and documentation
Nice to have
- Background in DataOps concepts and tools such as Airflow and dbt
- Knowledge of data governance platforms like Collibra
- Familiarity with Big Data technologies including Hadoop and Hive
- Certifications in cloud platforms or data engineering
We offer
- Opportunity to work on technical challenges that may impact across geographies
- Vast opportunities for self-development: online university, knowledge sharing opportunities globally, learning opportunities through external certifications
- Opportunity to share your ideas on international platforms
- Sponsored Tech Talks & Hackathons
- Unlimited access to LinkedIn learning solutions
- Possibility to relocate to any EPAM office for short and long-term projects
- Focused individual development
- Benefit package:
- Health benefits
- Retirement benefits
- Paid time off
- Flexible benefits
- Forums to explore beyond work passion (CSR, photography, painting, sports, etc.)
