Responsible for designing, developing, and deploying Artificial Intelligence (AI solutions) that can process structured and unstructured data from various sources to reason about them, answer queries about them, and synthesize information from them in specific formats. Experience with Natural Language Processing (NLP) models, Large Language Models (LLMs), Retrieval Augmented, Cache Augmented Generation (RAG/CAG), Chain of thoughts and other reasoning models in the context of Agentic AI would be required for success in this role. Collaborating with cross-functional teams, Research Engineer would integrate AI systems into existing software and infrastructure. The AI Research Engineer would research existing tools from the market and suggest available Commercial as well as Open-Source tools that could potentially be used to harness relevant information intelligently from various unstructured and structured data. - Develop and implement AI models. Design and implement AI, Agentic AI models using small language models and large language models to solve problems and enhance functionality. - Collaborate with cross-functional teams. Work with research engineers, research scientists, solution / technical architect, data scientists, stakeholders, project leads and managers to build and deploy AI solutions. - Optimize AI systems. Improve the performance of AI models and systems for scalability and real-time applications. - Prototyping and testing. Create prototypes to test AI applications and refine machine learning techniques. - Maintain AI pipelines. Develop and maintain pipelines for deploying AI models into production environments. - Stay updated on advancements. Research and implement the latest advancements in AI and machine learning frameworks. - Integrate AI solutions. Implement AI into existing software systems to enhance functionality and user experience. - B. Tech/ M. Tech / B.E / M.E in Computer Science, Computer Engineering, AI/ML, IT, Electronics and / or related degree with research / work experience in AI/ML. - Demonstrated experience in creating Natural Language Processing (NLP) solutions using Deep Learning techniques, with a focus on integrating Large Language Models/ SLMs and Retrieval Augmentation Generations (RAGs). - Proficient in Python and Deep Learning frameworks such as Tensorflow or PyTorch, and platforms for Generative AI. - Experience with training and fine-tuning large language models using large-scale datasets and distributed computing environments. - Excellent problem-solving skills, attention to detail, analytical thinking, and a collaborative mindset to work effectively within a fast-paced and innovative environment.