Senior Systems Engineer - Data DevOps/MLOps
We are looking for a dedicated and proficient Senior Systems Engineer with extensive Data DevOps/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.
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
- 5+ 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
