Azure AI/ML Lead Full-time

Description

Must Have:

• Strong Python skills: Expertise in Python for data exploration, analysis, and development using libraries like Pandas, Matplotlib, and Sci-kit Learn. Experience with Jupyter Notebook for interactive coding.
• NLP expertise: Deep understanding of natural language processing concepts and experience using Hugging Face pipelines for tasks like text classification, generation, and entity extraction.
• LLM experience: Hands-on experience with LLM frameworks like LLamaIndex or Langchain to build semantic search, retrieval-augmented generation (RAG), and hybrid search systems.
• Prompt Engineering: Ability to design and structure prompts for LLMs programmatically using APIs from OpenAI, Vertex AI, or Llama. Familiarity with common prompt engineering patterns.
• Vector Database knowledge: Experience with any vector databases like PineCone, Qdrant, Vespa, or Weaviate for efficient similarity search and retrieval.
• NLP evaluation metrics: Familiarity with common metrics used to evaluate the performance of NLP models, RAG systems particularly for retrieval and generation tasks.
• Azure AI Services experience: Experience with key Azure AI services such as Azure OpenAI Service, Azure AI Search, and Azure Document Intelligence for building intelligent solutions.
• Azure resource management: Proficiency in provisioning, configuring, and managing Azure AI resources, including understanding cost management and security best practices.
• Azure DevOps: Experience integrating Azure AI services into continuous integration and continuous delivery (CI/CD) pipelines for efficient deployment and updates.
• Containerization: Experience with containerized deployments on Azure for scalable and portable AI solutions.

Nice to Have:

• Conversation AI platforms: Familiarity with conversation AI platforms like Kore AI, RASA, Google Dialogflow, or CCAI for building conversational agents and chatbots.
• Approximate Nearest Neighbor libraries: Experience with libraries like FAISS or ANNOY for efficient approximate nearest neighbor search, particularly for large datasets.
• Advanced Prompting techniques: Understanding of advanced prompting techniques like ReAct, Few-shot learning, Chain-of-thought, function calling, and Responsible AI to enhance LLM performance and safety.