Lead Systems Engineer - Data DevOps/MLOps

We are seeking a skilled and passionate Lead Systems Engineer with Data DevOps/MLOps expertise to drive innovation and efficiency across our data and machine learning operations.
Responsibilities
Design, deploy, and manage CI/CD pipelines for seamless data integration and ML model deployment
Establish robust infrastructure for processing, training, and serving machine learning models using cloud-based solutions
Automate critical workflows such as data validation, transformation, and orchestration for streamlined operations
Collaborate with cross-functional teams, including data scientists and engineers, to integrate ML solutions into production environments
Improve model serving, performance monitoring, and reliability in production ecosystems
Ensure data versioning, lineage tracking, and reproducibility across ML experiments and workflows
Identify and implement opportunities to improve scalability, efficiency, and resilience of the infrastructure
Enforce rigorous security measures to safeguard data and ensure compliance with relevant regulations
Debug and resolve technical issues in data pipelines and ML deployment workflows
Requirements
Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
8+ years of experience in Data DevOps, MLOps, or related disciplines
Expertise in cloud platforms such as Azure, AWS, or GCP
Skills in Infrastructure as Code tools like Terraform, CloudFormation, or Ansible
Proficiency in containerization and orchestration technologies such as Docker and Kubernetes
Hands-on experience with data processing frameworks including Apache Spark and Databricks
Proficiency in Python with familiarity with libraries including Pandas, TensorFlow, and PyTorch
Knowledge of CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions
Experience with version control systems and MLOps platforms including Git, MLflow, and Kubeflow
Understanding of monitoring and alerting tools like Prometheus and Grafana
Strong problem-solving and independent decision-making capabilities
Effective communication and technical documentation skills
Nice to have
Background in DataOps methodologies and tools such as Airflow or dbt
Knowledge of data governance platforms like Collibra
Familiarity with Big Data technologies such as Hadoop or Hive
Showcase of certifications in cloud platforms or data engineering tools

Work arrangement
No