Lead Data Scientist GenAI

Job Description:
If you are a smart, self-motivated Machine Learning Scientist with a passion for advancing the field of Generative AI, we have an excellent opportunity for you. We are seeking candidates with deep expertise in developing and fine-tuning LLMs, RAG, agentic solutions, and knowledge graph technologies to drive innovative solutions in GenAI. You will be at the forefront of pioneering advancements in AI, working alongside some of the brightest minds in an exciting R&D environment to build cutting-edge capabilities that redefine the future of artificial intelligence.

Job Responsibilities:

• Lead research and development initiatives in the GenAI domain, focusing on cutting-edge technologies like Large Language Models, Retrieval-Augmented Generation, and autonomous agents.
• Design and implement advanced workflows for integrating LLMs into real-world applications across various domains such as Finance, Insurance, and Healthcare.
• Develop and fine-tune domain-specific LLMs to optimize performance, using techniques like prompt engineering, adapter-based tuning, or low-rank adaptation.
• Architect and implement scalable knowledge graph solutions to enhance reasoning capabilities in AI systems.
• Drive the development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques.
• Collaborate with cross-functional teams to prototype, test, and deploy solutions in production environments, both on-premise and in the cloud.
• Mentor and guide junior researchers and engineers to foster innovation and ensure high-quality deliverables.
• Stay at the forefront of AI advancements by reading, adapting, and implementing cutting-edge research to solve real-world challenges.
• Document research findings, methodologies, and implementations for internal and external stakeholders.

Qualifications:
Experience: 9-13 years in AI/ML research and development, with at least 2-3 years focusing on GenAI, 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, including retrieval system design and document store integration.
• Strong background in knowledge graph development, graph neural networks, and integrating graph-based reasoning in AI pipelines.
• Hands-on experience developing and deploying autonomous agents for complex problem-solving tasks.
• 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) and MLOps 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, including VAEs, GANs, or diffusion 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 reinforcement learning and fine-tuning via RLHF (Reinforcement Learning with Human Feedback).
• Knowledge of large-scale optimization methods for model training and inference.
• Familiarity with knowledge distillation and efficient model compression techniques.
• Strong collaboration and communication skills, with a proven ability to lead teams.