Generative AI

Responsibilities: Develop and Propose the solution architecture for Generative AI (especially LLMs), advanced Conversational AI chatbots & cloud AIaaS. Develop and implement applications leveraging advanced Generative AI models such as OpenAI GPT, Anthropic Clude, Meta Llama2 and Google Gemini focusing on enhancing developer and business productivity. Develop, Train, Finetune, and Deploy large language models to perform the domain specific tasks. Apply instruction tuning, reinforcement learning from human feedback (RLHF), and parameter efficient finetuning such as, adaptors, LoRA, and so on to improve LLMs for different use cases. Architect solutions incorporating advanced techniques like Retrieval Augmented Generation (RAG), Transformer Architectures, Lanchain, Sqlchain ensuring optimal model performance and scalability. Hands on experience in RAG system, LLM fine tuning and Vector DB is must Measure and benchmark of the various LLMs and application performance. Drive end-to-end implementation and deployment with extensive knowledge of Azure and AWS services Stay abreast of emerging trends, complex patterns, data dependencies, and advancements in AI architecture, contributing to the refinement and innovation of application development processes. Qualifications: Bachelor Degree or Masters degree (or equivalent experience) or PhD in Computer Science, Electrical Engineering, Artificial Intelligence. Excellent programming skills in Python with strong fundamentals in programming, optimizations and software design Strong knowledge of ML/DL techniques, algorithms, and tools with exposure to CNN, RNN (LSTM), Transformers (BERT, BART, GPT/T5, Megatron, LLMs) Hands-on experience on conversational AI Technologies like Natural Language Understanding, Natural Language Generation, Dialog systems (including system integration, state tracking and action prediction), Information retrieval and Question and Answering, Machine Translation etc. Experience with Training BERT, GPT and Megatron Models for different NLP and dialog system tasks using “PyTorch” Deep Learning Frameworks and performing NLP data wrangling and tokenization Understanding of MLOps life cycle and experience with MLOps workflows & traceability and versioning of datasets including knowhow of database management and queries (in SQL, MongoDB etc) Experience using end-to-end MLOps platform such as Kubeflow, MLFlow, AirFlow Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic matrix environment.