AI/ML Applications Development

Generative AI & LLM Expertise

• Extensive hands-on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open-source LLMs.
• Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementation.
• Proven ability to build, tune, and deploy LLM-based applications using platforms like Vertex AI, Hugging Face, etc.
• Expertise in developing robust prompt engineering strategies, prompt tuning, and creating reusable prompt templates.
• Hands-on experience with agentic framework-based use case implementation.
• Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.

Programming & Data Engineering

• Strong programming proficiency in Python is a must, including extensive experience with libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers, FastAPI, Seaborn, LangChain, and LlamaIndex.
• Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration tools.
• Hands-on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retrieval.
• Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processing.