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ML Ops
4 weeks ago
Position:ML Ops - Data Engineer
Location:Pune
Experience Range:2-4 years
- High-Level Pipeline Understanding: Ability to comprehend the DS pipeline architecture, including identifying table sources, understanding ML model usage, and tracking where model outputs are written.
- Python and SQL skills must be developed.
- Troubleshooting Expertise: Capable of diagnosing and resolving issues when pipelines fail, with hands-on experience in Python and Spark-based workflows.
- Databricks Experience: Proficient in operating within the Databricks environment, including running and monitoring pipelines, debugging errors, and optimizing workflows.
- Effective Communication: Strong written and verbal communication skills to document pipeline workflows, articulate technical issues, and collaborate with cross-functional teams.
- Collaborative Problem Solver: A proactive mindset to work with data science and engineering teams, ensuring pipeline reliability and efficiency.
- Familiarity with Linux systems and running Python environments locally.
- Understanding of CI/CD practices for ML pipelines (good to have)
- Experience with model monitoring and logging tools like MLFlow (good to have)
- Proficiency in cloud-based platforms such as Databricks, and AWS (good to have)