Description

You will bridge the gap between data science and production engineering by building infrastructure and workflows for machine learning models.

Responsibilities

• Build and maintain infrastructure, tooling, and workflows to develop, test, deploy, and monitor ML models at scale.
• Develop scalable applications from scratch using Python and backend frameworks.
• Develop and consume RESTful web services and APIs.
• Implement DevOps processes, CI/CD pipelines, and messaging services using Kafka.
• Execute site reliability engineering tasks, including monitoring migration scripts and validating observability dashboards.

Required Skills

• 5+ years of professional programming experience with Python.
• Hands-on experience with Apache Airflow and Apache Kafka.
• Proficiency in Kubernetes and container orchestration.
• Experience managing CI/CD pipelines and GitHub workflows.
• Practical knowledge of SRE and observability concepts including metrics, alerts, and logs.
• Experience with monitoring tools such as Grafana, Prometheus, or Kibana.
• Ability to develop backend services and RESTful APIs.
• Any Graduate degree.

Preferred Skills

• Experience deploying real-time inference services and batch prediction pipelines.
• Familiarity with model explainability, fairness, and responsible AI practices.
• Exposure to feature stores or experiment tracking platforms.

Key Skills

Education

Any Graduate