OpenStack QA

JD:

KEY RESPONSIBILITIES

• Conversant with the development and deployment of Python web services and APIs for different architectures, such as synchronous and asynchronous REST API calling, eventdriven ML product architectures and batch processing architectures for reporting and BI use cases
• Conversant with Elastic stack (Elastic, Logstash, Kibana) and other similar NoSQL data stores or databases
• Ability to develop and maintain code base of Python APIs with frameworks like Flask, Django, Fast API, with performant and readable code, good test coverage and adherence to code standards.
• Conversant with Git, CI/CD, Docker, Jenkins, Kubernetes, CI/CD pipelines and the deployment of applications
• Logging and monitoring using tools such as Azure Log Analytics, Monitor, Prometheus
• Knowledge and familiarity with test driven development in Python
• Familiar with the sequential development, testing and promotion of code through different environments, leading up to production release.
• Desirable to have working knowledge of the data science lifecycle and the LLMOps lifecycle from data to models, deployment, and use of models in applications
• Desirable to have knowledge of prompt engineering for working with LLM backends such as OpenAI, Llama and knowledge of frameworks such as Langchain and LlamaIndex
• Desirable if the candidate is conversant with parallel processing paradigms in Python, such as Thread and Process pools, and their use in parallelization of Python APIs and workloads
• Candidate should be able to work with or scale up to high level of coding standards and deliver Python APIs that can be scaled in their deployment.
• Candidate is expected to contribute ideas and best practices to the development and maintenance of Python APIs used across the product.
• Effectively collaborates with different team members such as data scientists, engineers, and architects to bring data and ML models to production.
• Clear verbal and written communication skills in English, and the ability to interact well with a globally distributed and diverse data science and engineering team.
• 5-8 years of Python development and back-end engineering experience, and at least a year of experience in shipping production-grade Python code.