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

Work arrangement
No