1. DataOPS: Proficiency in Python Core/Advanced for development and data pipelining.
Strong understanding of data structures Pandas Numpy sklearn concurrency and design patterns.
2. DevOPS: Experience in deploying applications using CI/CD tools such as Jenkins Jfrog Docker Kubernetes and Openshift Container Platform.
3. Microservices & REST APIs:
Familiarity with FastAPI Flask and Tornado for developing microservices and REST APIs.
4. Cloud:
Knowledge of building and deploying applications using cloud platforms.
5. Databases & SQL: Proficiency in working with databases such as Postgres Clickhouse and MongoDB.
6. Caching & Queuing: Experience with Pub/Sub (RabbitMQ) Redis and Diskcache for caching and queuing purposes.
7. Operating system: Strong understanding of both Linux and Windows operating systems.
8. Monitoring and Logging: Familiarity with Splunk for monitoring and logging applications.
Good to have skills include:
1. Generative AI knowledge: Knowledge of the Langchain framework and ChatGPT for generative AI applications.
2. MLOPS knowledge: Experience with Databricks MLFlow Kubeflow and ClearML for managing machine learning operations.
3. Testing knowledge: Proficiency in integration testing Python Behave and Pytest for ensuring code quality.
4. Maintaining code quality standards: Working knowledge of Pylint for maintaining code quality standards.
5. Logging: Familiarity with Kibana and Elastic search for advanced logging and analysis.
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