Senior Systems Engineer - Data DevOps/MLOps

As a Senior Systems Engineer specializing in Data DevOps/MLOps, you will play a crucial role in our team by leveraging your expertise in data engineering, automation for data pipelines, and operationalizing machine learning models. This position requires a collaborative professional who can design, deploy, and manage CI/CD pipelines for data integration and machine learning model deployment. You will be responsible for building and maintaining infrastructure for data processing and model training using cloud-native tools and services. Your role will involve automating processes for data validation, transformation, and workflow orchestration, ensuring seamless integration of ML models into production. You will work closely with data scientists, software engineers, and product teams to optimize performance and reliability of model serving and monitoring solutions. Managing data versioning, lineage tracking, and reproducibility for ML experiments will be part of your responsibilities. You will also identify opportunities to enhance scalability, streamline deployment processes, and improve infrastructure resilience. Implementing security measures to safeguard data integrity and ensure regulatory compliance will be crucial, along with diagnosing and resolving issues throughout the data and ML pipeline lifecycle. To qualify for this role, you should hold a Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field, along with 4+ years of experience in Data DevOps, MLOps, or similar roles. Proficiency in cloud platforms like Azure, AWS, or GCP is required, as well as competency in using Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, or Ansible. Expertise in containerization and orchestration technologies like Docker and Kubernetes is essential, along with a background in data processing frameworks such as Apache Spark or Databricks. Skills in Python programming, including proficiency in data manipulation and ML libraries like Pandas, TensorFlow, and PyTorch, are necessary. Familiarity with CI/CD tools such as Jenkins, GitLab CI/CD, or GitHub Actions, as well as understanding version control tools like Git and MLOps platforms such as MLflow or Kubeflow, will be valuable. Knowledge of monitoring, logging, and alerting systems (e.g., Prometheus, Grafana), strong problem-solving skills, and the ability to contribute independently and within a team are also required. Excellent communication skills and attention to documentation are essential for success in this role. Nice-to-have qualifications include knowledge of DataOps practices and tools like Airflow or dbt, an understanding of data governance concepts and platforms like Collibra, and a background in Big Data technologies like Hadoop or Hive. Qualifications in cloud platforms or data engineering would be an added advantage.,