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
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