Senior Systems Engineer - Data DevOps/MLOps

We are looking for a detail-oriented and motivated Senior Systems Engineer with a strong focus on Data DevOps/MLOps to join our team.

The ideal candidate should possess a deep understanding of data engineering, automation of data pipelines, and integration of machine learning models into operational environments. This role is for a collaborative professional adept at building, deploying, and managing scalable data and ML pipelines aligned with strategic objectives.

Responsibilities

• Design CI/CD pipelines for data integration and machine learning model deployment
• Deploy and maintain infrastructure for data processing and model training using cloud services
• Automate processes like data validation, transformation, and workflow orchestration
• Coordinate with data scientists, software engineers, and product teams to integrate ML models into production environments
• Enhance performance and reliability by optimizing model serving and monitoring processes
• Ensure data versioning, lineage tracking, and reproducibility across ML experiments
• Identify improvements for deployment processes, scalability, and infrastructure resilience
• Implement security measures to safeguard data integrity and maintain compliance
• Resolve issues in the data and ML pipeline lifecycle

Requirements

• Bachelors or Masters degree in Computer Science, Data Engineering, or a related field
• 5 or more years of experience in Data DevOps, MLOps, or related professions
• 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 tools such as Docker and Kubernetes
• Skills in using data processing frameworks like Apache Spark or Databricks
• Proficiency in Python, with familiarity with data manipulation and ML libraries such as Pandas, TensorFlow, or PyTorch
• Familiarity with CI/CD tools like Jenkins, GitLab CI/CD, or GitHub Actions
• Knowledge of version control systems, such as Git, and MLOps platforms like MLflow or Kubeflow
• Understanding of monitoring, logging, and alerting systems like Prometheus or Grafana
• Strong problem-solving abilities with the capability to work both independently and collaboratively
• Effective communication and documentation skills

Nice to have

• Familiarity with DataOps practices and tools like Airflow or dbt
• Understanding of data governance frameworks and tools like Collibra
• Knowledge of Big Data technologies such as Hadoop or Hive
• Credentials in cloud platforms or data engineering activities