Are you excited about working at the intersection of Data, Artificial Intelligence, and Cloud technologies?
Your mision will be bring Data & AI solutions into production while ensuring scalability, automation, observability, and reliability.
What will you do?
• Build and maintain data pipelines and AI model deployments.
• Automate integration and deployment processes through CI/CD practices.
• Monitor pipelines, models, and services to identify incidents, deviations, and performance issues.
• Manage model traceability, versioning, and technical documentation.
• Improve automation across the data and model lifecycle.
• Collaborate with Architecture, Infrastructure, Security, Engineering, and Data Science teams.
• Drive continuous improvements in DataOps, MLOps, and AIOps practices and tooling.
Requirements:
• 3+ years of experience in Data Engineering, DataOps, MLOps, DevOps, or similar roles.
• Hands-on experience with Git, CI/CD, and deployment automation.
• Strong Python and SQL skills.
• Experience with AWS, Azure, or GCP.
• Knowledge of Docker and Kubernetes.
• Experience with orchestration tools such as Airflow, Prefect, or Dagster.
• Background in monitoring, observability, and troubleshooting.
• Experience deploying, versioning, and monitoring AI/ML models.
• Ability to work cross-functionally with multiple technical teams.
• Experience with MLflow, Terraform, Cloudera, Prometheus, and Grafana.
• Knowledge of Data Quality frameworks such as Great Expectations or Deequ.
• Familiarity with BI governance tools (Power BI, Tableau, or similar).
• Experience with Generative AI, LLMOps, and regulated or financial environments.
• Degree in Engineering, Computer Science, Information Systems, Data Science, or related fields.
