77 Bert Jobs - Page 4

Role: Lead ML Developer (NLP) Job Summary: This job requires candidate to lead the design and development work with Large Language model and application of Large Language models for different use cases. As an expert in using Generative AI, particularly LLM models, and proficient in Python programming, he/she will be responsible for developing and deploying a system that can convert natural language queries to handle the use cases like building SQL query statements for searching tables in DB or searching the documents for Q&A. This role requires a deep understanding of natural language processing (NLP) techniques, database management systems, SQL Query optimization, and passion for building intelligent systems. Responsibilities: Develop and implement a robust system for converting natural language queries with help of prompt engineering into useful format, which can be further programmatically used for search a set of SQL tables or set of documents. Development and application of word embedding techniques (Woprd2vec, Transformer model based encoders, LLM based encoder, BERT based encoders etc) Fine Tuning of Large Language Models with custom data sets. Utilize Generative AI techniques, leveraging Large Language models, to accurately interpret and convert natural language queries into SQL statements. Use of LLMs for the NLP state of art techniques like Text Classification, NER, Keyword Extraction, Text-to-SQL conversion. Check the feasibility of use cases based on Large Language Models, specifically in area chatbot for sales and finance problems. Develop mechanism to summarize SQL tables into user-level summary text, providing concise and meaningful insights from the data retrieved. Design and build scalable architecture that can handle a large volume of queries efficiently, ensuring high performance and minimal latency. Collaborate with cross-functional teams, including data scientists, software engineers, and database administrators, to understand requirements and integrate the solution into existing systems. Conduct thorough research and stay up to date with the latest advancements in NLP, machine learning, and Generative AI to continuously improve the systems accuracy and efficiency. Optimize and fine-tune SQL queries to ensure efficient data retrieval, taking into account query execution plans, indexes, and query performance optimization techniques. Develop testing frameworks and conduct rigorous testing to validate the systems accuracy, reliability, and scalability. Document the system architecture, design decisions, and codebase to facilitate future maintenance and enhancements. Skills MUST Strong experience in architecting and development of ML, NLP based projects is a must. Strong track record of ML led solution development from scratch. Strong proficiency in Python Programming (very strong Python credentials only apply) and experience with relevant libraries and frameworks for NLP such as NLTK, spaCy, Hugging Face transformers. In-depth knowledge and experience in using Generative AI techniques for NLP tasks, preferable with Large Language models (e.g., GPT-3, GPT-4), GCP PALM models (code bison, text bison) or Hugging Face models. Good exposure to word embedding techniques. (BERT, Word2Vec, LLM based encoders etc.) Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and deep learning architectures. Hands on experience with Langchain framework. Solid understanding of SQL and experience working with popular relational database management systems. Proficiency in writing both simple standard SQL queries and complex joining queries to fetch data from a database. Experience with query optimization techniques and understanding of indexes, execution plans, and performance tuning. Familiarity with cloud-based data warehousing platforms. Strong problem-solving skills and ability to translate business requirements into technical solutions. Excellent communication skills to collaborate effectively with multidisciplinary teams. Ability to work independently, manage priorities, and deliver high-quality results within project timelines. Strong attention to detail and a commitment to producing clean, well-documented code.

We are looking for a Senior Machine Learning Engineer with deep expertise in Transformers, Large Language Models (LLMs), and Natural Language Processing (NLP). This role involves designing, training, and fine-tuning state-of-the-art AI models for real-world applications. The ideal candidate will have a strong research background and hands-on experience in deploying scalable NLP solutions. Key Responsibilities Research, develop, and optimize Transformer-based architectures (e.g., BERT, GPT, T5, LLaMA) for various NLP tasks. Fine-tune LLMs on domain-specific datasets to improve accuracy and performance. Work on text generation, summarization, named entity recognition (NER), and semantic search applications. Implement and optimize embedding techniques for retrieval-augmented generation (RAG). Apply self-supervised and reinforcement learning techniques to enhance model performance. Deploy and scale ML models using cloud platforms (AWS, GCP, Azure) and containerized solutions like Docker and Kubernetes. Improve inference efficiency using quantization, distillation, and model optimization techniques. Collaborate with data engineers, software developers, and research scientists to integrate ML models into production. Stay updated with the latest advancements in AI, NLP, and Deep Learning, applying innovative techniques to solve business challenges. Required Skills & Qualifications Expertise in NLP & LLMs: Strong understanding of transformer-based models (e.g., BERT, GPT, T5, LLaMA). Programming Skills: Proficiency in Python and deep learning frameworks like PyTorch, TensorFlow, and Hugging Face Transformers. Model Optimization: Experience with quantization, pruning, and distillation to improve model efficiency. Data Handling: Strong experience in preprocessing, tokenization, and vectorization of large text datasets. Deployment & Scalability: Hands-on experience with MLOps, API development, cloud services (AWS, GCP, Azure), and containerization (Docker, Kubernetes). Information Retrieval & RAG: Knowledge of vector databases (FAISS, Pinecone, Weaviate) and embedding techniques. Mathematical Foundation: Strong background in linear algebra, probability, and deep learning architectures. Collaboration: Ability to work with cross-functional teams and communicate technical concepts effectively. Preferred Qualifications Experience in low-rank adaptation (LoRA) and fine-tuning LLMs with limited resources. Exposure to multimodal learning (text, images, audio). Research publications or contributions to open-source NLP projects. Familiarity with prompt engineering and fine-tuning for AI assistants. What We Offer Opportunity to work on cutting-edge AI and NLP projects with a talented team. Ability to shape the development of next-generation AI applications. Access to latest ML research, conferences, and learning resources. Flexible work arrangements (remote/hybrid options available). Competitive salary and performance-based incentives.