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 field4 or more years of experience in Data DevOps, MLOps, or related professionsProficiency in cloud platforms such as Azure, AWS, or GCPBackground in Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or AnsibleExpertise in containerization and orchestration tools such as Docker and KubernetesSkills in using data processing frameworks like Apache Spark or DatabricksProficiency in Python, with familiarity with data manipulation and ML libraries such as Pandas, TensorFlow, or PyTorchFamiliarity with CI / CD tools like Jenkins, GitLab CI / CD, or GitHub ActionsKnowledge of version control systems, such as Git, and MLOps platforms like MLflow or KubeflowUnderstanding of monitoring, logging, and alerting systems like Prometheus or GrafanaStrong problem-solving abilities with the capability to work both independently and collaborativelyEffective communication and documentation skillsNice to have
Familiarity with DataOps practices and tools like Airflow or dbtUnderstanding of data governance frameworks and tools like CollibraKnowledge of Big Data technologies such as Hadoop or HiveCredentials in cloud platforms or data engineering activitiesSkills Required
Airflow, Prometheus, Grafana, Tensorflow, Pytorch, Docker, Terraform, Python, Aws, Hadoop, Collibra, Cloudformation, Apache Spark, Jenkins, Git, Hive, Gcp, Pandas, Ansible, dbt, Databricks, Azure, Kubernetes