ML Ops engineer - Insurance Practice

Education, Technical Skills & Other Critical Requirement :

• 3-6 years of relevant experience in AI / analytics product & solution delivery
• Bachelor's / master's degree in an information technology / computer science / Engineering or equivalent fields experience.
• Proficiency in frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Strong skills in Python and / or R; familiarity with Java, Scala, or Go is a plus.
• Experience with cloud services such as AWS, Azure, or Google Cloud Platform, particularly in ML services (e.g., AWS SageMaker, Azure ML).
• CI / CD tools (e.g., Jenkins, GitLab CI), containerization (e.g., Docker), and orchestration (e.g., Kubernetes).
• Experience with databases (SQL and NoSQL), data pipelines, ETL processes, ML pipeline orchestration (Airflow)
• Familiarity with monitoring and logging tools such as Prometheus, Grafana, or ELK stack.
• Proficient in using Git for version control.
• Strong analytical and troubleshooting abilities to diagnose and resolve issues effectively.
• Good communication skills for working with cross-functional teams and conveying technical concepts to non-technical stakeholders.
• Ability to manage multiple projects and prioritize tasks in a fast-paced environment.

Skills Required

Tensorflow, Pytorch, Cloud Services, Python