Anuj Saini

Anuj Saini

Director Data Science

RPX

Anuj Saini is a Subject Matter Expert in Natural Language Processing (NLP), Search Technologies, Statistics, Analytics, Modelling, Data Science, and Machine Learning, with a strong emphasis on Large Language Models (LLMs) and Generative AI.

Anuj brings extensive experience in developing advanced AI systems, particularly NLP applications using state-of-the-art machine learning techniques across diverse domains such as e-commerce, investment banking, and insurance. His expertise includes cutting-edge AI technologies like ChatGPT, LangChain, LLama2, OpenAI Embeddings, and HuggingFace.

Specializing in building intelligent Chatbots, Recommender Systems, Sentiment Analysis, and Semantic Technologies, Anuj leverages his proficiency in Python to deliver innovative solutions. With a proven track record in designing and implementing sophisticated LLM-driven applications, he is recognized as a leader in the field of Generative AI and NLP.

In this practical session, participants will learn how to build autonomous AI agents using open-source LLMs and apply responsible AI principles through real-world guardrailing techniques. We will walk through the full pipeline — from creating a task-specific agent using LLaMA or Mistral-based models, to integrating NVIDIA NeMo Guardrails, Llama Guard, and prompt-based safety strategies.

We’ll cover critical safety challenges such as:

  • Prompt injection and jailbreaks
  • Toxicity and bias mitigation
  • Controlling agent autonomy and output

This session will include:

  • Setting up an AI agent for a real-world use case (e.g., customer support, knowledge
    assistant)
  • Injecting common adversarial prompts to test vulnerabilities
  • Applying NVIDIA NeMo Guardrails and Llama Guard to detect and prevent harmful
    outputs
  • Using prompt-based guardrailing as a first line of defense
  • Discussing practical limitations and failure cases in alignment and safety
Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More