Responsibilities :

- Design and implement advanced solutions utilizing Large Language Models (LLMs).

- Demonstrate self-driven initiative by taking ownership and creating end-to-end solutions.

- Conduct research and stay informed about the latest developments in generative AI and LLMs.

- Develop and maintain code libraries, tools, and frameworks to support generative AI development.

- Participate in code reviews and contribute to maintaining high code quality standards.

- Engage in the entire software development lifecycle, from design and testing to deployment and maintenance.

- Collaborate closely with cross-functional teams to align messaging, contribute to roadmaps, and integrate software into different repositories for core system compatibility.

- Possess strong analytical and problem-solving skills.

- Demonstrate excellent communication skills and the ability to work effectively in a team environment.

Primary Skills :

- Natural Language Processing (NLP) : Hands-on experience in use case classification, topic modeling, Q&A and chatbots, search, Document AI, summarization, and content generation.

- Computer Vision and Audio : Hands-on experience in image classification, object detection, segmentation, image generation, audio, and video analysis.

- Generative AI : Proficiency with SaaS LLMs, including Lang chain, llama index, vector databases, Prompt engineering (COT, TOT, ReAct, agents). Experience with Azure OpenAI, Google Vertex AI, AWS Bedrock for text/audio/image/video modalities.

- Familiarity with Open-source LLMs, including tools like TensorFlow/Pytorch and huggingface. Techniques such as quantization, LLM finetuning using PEFT, RLHF, data annotation workflow, and GPU utilization.

- Cloud : Hands-on experience with cloud platforms such as Azure, AWS, and GCP. Cloud certification is preferred.

- Application Development : Proficiency in Python, Docker, FastAPI/Django/Flask, and Git.

Tech Skills (10+ Years Experience) :

- Machine Learning (ML) & Deep Learning

- Solid understanding of supervised and unsupervised learning.

- Proficiency with deep learning architectures like Transformers, LSTMs, RNNs, etc.

- Generative AI :

- Hands-on experience with models such as OpenAI GPT4, Anthropic Claude, LLama etc.

- Knowledge of fine-tuning and optimizing large language models (LLMs) for specific tasks.

- Natural Language Processing (NLP) :

- Expertise in NLP techniques, including text preprocessing, tokenization, embeddings, and sentiment analysis.

- Familiarity with NLP tasks such as text classification, summarization, translation, and question-answering.

- Retrieval-Augmented Generation (RAG) :

- In-depth understanding of RAG pipelines, including knowledge retrieval techniques like dense/sparse retrieval.

- Experience integrating generative models with external knowledge bases or databases to augment responses.

Data Engineering :

- Ability to build, manage, and optimize data pipelines for feeding large-scale data into AI models.

- Search and Retrieval Systems :

- Experience with building or integrating search and retrieval systems, leveraging knowledge of Elasticsearch, AI Search, ChromaDB, PGVector etc.

- Prompt Engineering :

- Expertise in crafting, fine-tuning, and optimizing prompts to improve model output quality and ensure desired results.

- Understanding how to guide large language models (LLMs) to achieve specific outcomes by using different prompt formats, strategies, and constraints.

- Knowledge of techniques like few-shot, zero-shot, and one-shot prompting, as well as using system and user prompts for enhanced model performance.

- Programming & Libraries :

- Proficiency in Python and libraries such as PyTorch, Hugging Face, etc.

- Knowledge of version control (Git), cloud platforms (AWS, GCP, Azure), and MLOps tools.

- Database Management :

- Experience working with SQL and NoSQL databases, as well as vector databases

- APIs & Integration :

- Ability to work with RESTful APIs and integrate generative models into applications.

- Evaluation & Benchmarking :

- Strong understanding of metrics and evaluation techniques for generative models.