Senior Systems Engineer - Data DevOps/MLOps

Job description

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** - Bachelor’s or Master’s 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