Senior Consultant

Generative AI & Large Language Models
Work with advanced GenAI models and LLMs (eg, ChatGPT, FlanT-5, Llama, Cohere) to develop innovative solutions for chatbot systems, content summarization, and other AI-driven applications.


Fine-tune and optimize pre-trained models for specific business use cases.


Implement Reinforcement Learning with Human Feedback (RLHF) to improve model performance and user interaction quality.


Model Development & Fine-Tuning
Design and develop vision models and multi-agent systems.


Conduct prompt engineering to improve language models accuracy and response relevance.


Apply techniques like retrieval-augmented generation (RAG) to enhance model responses with external data.


AI Systems & Applications
Design and deploy scalable AI applications using platforms such as Langchain, Hugging Face, and Streamlit.


Develop and integrate chatbots, summarization tools, and other NLP-based applications.


Apply FMEA (Failure Mode and Effect Analysis) to model design to ensure robustness and reliability.


Machine Learning & Deep Learning
Develop and deploy machine learning models for various tasks, including regression, classification, and neural networks.


Build and optimize deep learning models to solve complex problems across industries.


Cloud AI Deployment
Deploy machine learning models at scale using AWS SageMaker and AWS Bedrock for efficient model training and inference.


Collaborate with cloud engineers to ensure seamless integration with cloud infrastructure and services.


Collaboration & Documentation
Work closely with cross-functional teams, including data scientists, engineers, product managers, and business stakeholders.


Maintain thorough documentation of AI models, training procedures, model versions, and deployment processes.


Mentor junior engineers and data scientists, sharing expertise in AI/ML technologies and best practices.


Familiarity with crewAI, Comet for AI model tracking and collaboration.


Experience in working with MongoDB or other NoSQL databases to store model-related data.


Familiarity with the latest advancements in ontology and its application in AI systems.


Understanding of multi-agent systems and how they can be used for distributed problem-solving.


Knowledge of AWS Lambda and API Gateway for serverless deployment of AI models.


Generative AI & LLMs
Experience with large language models like ChatGPT, FlanT-5, Llama, and Cohere.


Expertise in prompt engineering and fine-tuning LLMs for various applications (eg, chatbots, summarization).


Hands-on experience with Reinforcement Learning with Human Feedback (RLHF) techniques.


Machine Learning & Deep Learning
Strong foundation in machine learning techniques such as regression and classification.


Experience working with neural networks and deep learning models (CNNs, RNNs, Transformers).


Familiarity with common machine learning frameworks (eg, TensorFlow, PyTorch).


AI Frameworks & Libraries
Experience with Langchain, Hugging Face, and other frameworks for building and deploying AI models.


Familiarity with Streamlit for building interactive AI applications.


Experience in implementing RAG (retrieval-augmented generation) for more informative and accurate responses from models.


Cloud Platforms oExpertise in using AWS SageMaker for model training, hyperparameter tuning, and deployment.