Chennai, Tamil Nadu, India
Information Technology
Full-Time
RCM Business Solutions
Overview
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.
- 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.
- 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.
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