Job Description

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. 2. 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. 3. 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. 4. 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. 5. Data Engineering: - Ability to build, manage, and optimize data pipelines for feeding large-scale data into AI models. 6. Search and Retrieval Systems: - Experience with building or integrating search and retrieval systems, leveraging knowledge of Elasticsearch, AI Search, ChromaDB, PGVector etc. 7. 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. 8. 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. 9. Database Management: - Experience working with SQL and NoSQL databases, as well as vector databases 10. APIs & Integration: - Ability to work with RESTful APIs and integrate generative models into applications. 11. Evaluation & Benchmarking: - Strong understanding of metrics and evaluation techniques for generative models.

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