Role & responsibilities Conduct research in AI, focusing on LLMs, NLP, and Graph-based approaches Design and implement innovative algorithms and models to improve AI capabilities Collaborate with cross-functional teams to apply research findings to real-world applications Stay current with the latest advancements in AI and contribute to the company's intellectual property portfolio Key Skills : Large Language Models (e.g., Transformer architectures, few-shot learning, prompt engineering) Natural Language Processing (e.g., text classification, named entity recognition, sentiment analysis) Graph Neural Networks and graph-based machine learning Distributed computing and parallel processing Data analysis and visualization Scientific writing and presentation skills Required Qualifications: B. Tech / Masters in Computer Science, Artificial Intelligence, or a related field Prefer a Ph.D. in Computer Science, Artificial Intelligence, or a related field Strong background in machine learning, deep learning, and AI algorithms Experience in NLP techniques and Large Language Models Experience with Graph Neural Networks and graph-based machine learning approaches Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) Excellent programming and software engineering skills Preferred Qualifications: Ph.D. in Computer Science, Artificial Intelligence, or a related field Experience with distributed computing and large-scale machine learning systems Familiarity with cloud computing platforms (e.g., AWS, Google Cloud, Azure) Knowledge of ethical AI and responsible AI development practices Experience with multi-modal AI systems combining language, vision, and other modalities
Role & responsibilities Evaluate, fine-tune, and deploy production-ready models using the latest open-source and commercial model families (e.g., GPT-4o, Claude 3, Mixtral, LLaMA 3, Gemini). Develop and manage domain-specific generative models for tasks like summarization, classification, extraction, and generation using Transformers and LLMs. Build and maintain retrieval-augmented generation (RAG) pipelines, including vector databases (e.g., Weaviate, FAISS, LanceDB, Pinecone). Work with frameworks such as LangChain, LlamaIndex for prompt chaining, agent orchestration, and memory-enabled AI. Optimize for commercial KPIs such as inference cost, latency, model accuracy, and scalability. Keep up to date with the evolving landscape of AI/ML and recommend tools, models, or practices that can improve team capabilities. Preferred candidate profile Programming: Expert-level proficiency in Python and common ML/AI libraries (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, LangChain). LLMs & NLP: Deep experience working with transformer-based architectures and LLMs, including prompt engineering, fine-tuning, and instruction-tuning. Generative AI: Practical knowledge in building GenAI applications using both open-source (Mistral, LLaMA) and API-based (OpenAI, Anthropic) models. RAG & Vector Search: Experience with RAG architecture and vector databases like FAISS, Pinecone, or Qdrant. Machine Learning & Deep Learning: Solid understanding of supervised, unsupervised, and reinforcement learning algorithms.