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.