3 Knowledge Distillation Jobs

If you are a smart, self-motivated Machine Learning Scientist with a passion for advancing the field of Generative AI, an excellent opportunity awaits you. EXL, a rapidly expanding global digital data-led AI transformation solutions company, is seeking candidates with deep expertise in developing and fine-tuning Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic solutions, and knowledge graph technologies to drive innovative solutions in Generative AI. You will have the chance to be at the forefront of pioneering advancements in AI, working alongside bright minds in an exciting R&D environment to build cutting-edge capabilities that redefine the future of artificial intelligence. In this role, you will develop initiatives in the Generative AI domain, focusing on cutting-edge technologies like LLMs, RAG, and autonomous agents. You will design and implement advanced workflows for integrating LLMs into real-world applications across domains such as Finance, Insurance, and Healthcare. Additionally, you will drive the development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques. Keeping abreast of AI advancements is essential, as you will be required to read, adapt, and implement cutting-edge research to solve real-world challenges. Documenting research findings, methodologies, and implementations for internal and external stakeholders will also be part of your responsibilities. Qualifications: - Experience: 2-5 years in AI/ML research and development, with at least 1-2 years focusing on Generative AI, LLMs, or related fields. - Education: Masters or PhD in Computer Science, AI, or a related field from a top-tier institution is highly preferred. Required Skills: - Core Expertise: Proven experience with Large Language Models (e.g., GPT-4, BERT, LLaMA, PaLM) and fine-tuning them for domain-specific applications. In-depth knowledge of Retrieval-Augmented Generation workflows and hands-on experience with autonomous agents. - Tools & Frameworks: Proficiency in deep learning frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. Experience with distributed training and optimization on GPUs and TPUs. Familiarity with cloud ecosystems (AWS, Azure, Google Cloud) practices for scalable deployment. - Research & Development: Ability to read and adapt cutting-edge research papers for applied solutions in LLMs and knowledge graphs. Expertise in domain adaptation, few-shot learning, and zero-shot reasoning. Strong understanding of generative models and their integration with LLMs. - Problem Solving: Demonstrated ability to address challenges in unstructured data processing, including NLP and multimodal scenarios. Experience with document retrieval, clustering, and unsupervised learning techniques. Preferred Skills: - Experience with LLM fine-tuning and building Agentic systems for domain LLMs. Experience with reinforcement learning and fine-tuning via RLHF. Knowledge of large-scale optimization methods and efficient model compression techniques. Strong collaboration and communication skills, with a proven ability to lead teams. Experience with MoE based architecture and knowledge of federated learning.,

You are a Senior or Staff AI Scientist responsible for leading the development of domain-specific business ontologies and knowledge engineering infrastructure. Your expertise will be utilized to leverage cutting-edge LLMs, GenAI, and symbolic reasoning to establish an ontology layer, which will serve as the cornerstone of the data platform. This initiative aims to facilitate intelligent data harmonization, enrichment, and decision automation at a large scale. Your key responsibilities will involve spearheading the research and development of ontology learning systems by leveraging state-of-the-art LLMs, structured data mining, and semantic reasoning techniques. You will be tasked with constructing and expanding domain-specific knowledge graphs that seamlessly integrate both external and internal data sources. Additionally, you will drive innovation in semi-automated ontology construction, concept disambiguation, and alignment using GenAI, contrastive learning, and knowledge distillation methodologies. Collaboration across departments is crucial in embedding ontology-driven intelligence into various pipelines, applications, and decision systems. You will work closely with data platform engineers, AI scientists, and product teams to ensure seamless integration and implementation of these intelligent systems. Furthermore, you will be responsible for defining core metrics to evaluate the quality of ontologies, knowledge coverage, and subsequent performance enhancements such as data harmonization and semantic search capabilities. Your role will also involve collaborating with ML infrastructure teams to optimize representation formats (RDF, Knowledge graphs, etc.) and enable scalable, low-latency retrieval and reasoning processes. To qualify for this position, you should hold a PhD or Masters degree in Computer Science, AI, Data Science, or a related field with a focus on knowledge representation, natural language understanding, or data systems. Moreover, you should possess a minimum of 8 years of experience in applied AI or ML research with a specific focus on ontologies, knowledge engineering, entity resolution, or semantic systems. Your expertise should extend to working with LLMs (e.g., GPT, LLaMA, PaLM, Claude) for knowledge extraction, generation, or few-shot learning. A strong background in data engineering, including schema mapping, metadata, master data management, or data pipelines at scale, is also essential. You are expected to have a deep understanding of semantic data models such as ontologies and hands-on experience with relevant libraries or frameworks. Proficiency in programming languages like Python, as well as frameworks like PyTorch or TensorFlow, and graph technologies such as Neo4j, RDFLib, SPARQL, etc., will be beneficial in fulfilling the requirements of this role.,

Tata Electronics Pvt. Ltd. is a key global player in the electronics manufacturing industry, specializing in Electronics Manufacturing Services, Semiconductor Assembly & Test, Semiconductor Foundry, and Design Services. Established in 2020 by the Tata Group, the company's primary objective is to provide integrated solutions to global customers across the electronics and semiconductor value chain. We are looking for an AI Core Developer to join our R&D team in Bangalore. This role is centered around fundamental AI research, algorithm development, and model pre-training, focusing on innovation rather than application engineering. As an AI Core Developer, you will be involved in cutting-edge AI research, creating novel algorithms, and constructing foundation models from scratch. This position is ideal for individuals with a strong background in pre-training methodologies and algorithm development who aspire to contribute to core AI advancements. Your responsibilities will include developing and implementing innovative machine learning algorithms for various AI systems, designing pre-training pipelines for large models, prototyping new AI architectures, collaborating with research scientists and engineers, and contributing to technical publications. The ideal candidate should hold a Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field, with 2-4 years of hands-on experience in AI/ML development. Proficiency in Python, C/C++, knowledge of deep learning frameworks such as PyTorch and TensorFlow, and experience with model pre-training are essential requirements. Strong mathematical skills, familiarity with transformer architectures and attention mechanisms, and understanding of distributed computing are also key competencies. Preferred qualifications include advanced experience in multimodal AI systems, research contributions to top-tier AI conferences, and expertise in specific AI domains like healthcare or finance. The position is based in Bangalore, India, with a hybrid work arrangement and occasional travel for conferences and collaborations.,