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, Cloud.

Formation, 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, Tensor. Flow, or Py. Torch 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 Data.

Ops 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