Job Summary
Key Responsibilities
2. Develop RESTful APIs with Flask, Django, or FastAPI to enable seamless integration of LLM-based features into web and enterprise applications.
3. Integrate and manage data storage using MySQL, PostgreSQL, and VectorDB technologies such as Pinecone, ensuring efficient handling of embeddings and AI-generated content.
4. Apply solid understanding of Azure AI Search and vector databases to enhance retrieval-augmented generation and semantic search capabilities within GenAI applications.
5. Participate in technical discussions and feasibility studies by evaluating GenAI architectures, technical alternatives, and risk factors to support project delivery.
6. Ensure process compliance and contribute to technical documentation, status reporting, and risk mitigation for assigned modules.
Skill Requirements
2. Solid Experience With Python Programming For Ai Application Development.
3. Solid Knowledge Of Restful Api Design And Implementation Using Flask, Django, Or Fastapi.
4. Solid Skills In Relational Databases (Mysql, Postgresql) And Vector Databases (Pinecone, Vectordb).
5. Solid Understanding Of Embedding Techniques And Semantic Search Using Azure Ai Search.
6. Solid Ability To Analyze Technical Feasibility And Recommend Best Practices In Genai Solution Development.
Other Requirements
2. Certifications such as Microsoft Certified: Azure AI Engineer Associate
3. - Google Professional Machine Learning Enginee