AI Developer

Job Summary:

We are seeking a highly skilled and innovative AI Developer with strong expertise in Python and FastAPI, and hands-on experience with Azure AI services. The ideal candidate will have a deep understanding of Retrieval-Augmented Generation (RAG) pipelines, particularly involving video file processing, and be proficient in working with Milvus vector databases and Azure Content Understanding components.

Key Responsibilities:

Design, develop, and deploy scalable AI solutions using Python and FastAPI.

Implement and optimize RAG pipelines for multimodal data, especially video-based content.

Integrate and utilize Azure AI services, including Azure Content Understanding, Azure OpenAI, and Cognitive Search.

Architect and manage Milvus vector databases for efficient similarity search and semantic retrieval.

Collaborate with data scientists, ML engineers, and product teams to deliver intelligent applications.

Build APIs and microservices to expose AI capabilities securely and efficiently.

Ensure performance, scalability, and reliability of AI systems in production environments.

Stay updated with the latest advancements in AI, LLMs, and vector search technologies.

Required Qualifications:

Proven experience in Python and FastAPI development.

Strong understanding of RAG architecture and its application in real-world scenarios.

Experience with Azure AI components, especially Content Understanding and Cognitive Services.

Hands-on experience with Milvus or similar vector databases.

Familiarity with video processing pipelines and embedding generation from video content.

Knowledge of LLMs, embeddings, and semantic search techniques.

Experience with containerization (Docker) and cloud deployment (Azure preferred).

Excellent problem-solving and communication skills.

Preferred Qualifications:

Experience with LangChain, LLM orchestration frameworks, or custom RAG implementations.

Familiarity with transformer-based models and video-to-text or video summarization techniques.

Exposure to CI/CD pipelines, DevOps practices, and monitoring tools.

Contributions to open-source AI projects or publications in the field.