AI Research Engineer

About the Role

Work on the cutting edge of Agentic AI and Large Language Models. You will design, fine-tune, and deploy state-of-the-art LLMs for enterprise applications and autonomous workflows. This role combines research and engineering, requiring you to stay current with the latest AI breakthroughs while building production-ready systems that serve millions of users. Join us in shaping the future of AI-powered automation.

Responsibilities

• Fine-tune and deploy Large Language Models (LLMs) for domain-specific applications using techniques like LoRA, QLoRA, and full fine-tuning.
• Develop advanced Graph RAG (Retrieval-Augmented Generation) systems for complex knowledge retrieval and reasoning.
• Research and implement novel agentic architectures including ReAct, AutoGPT, and custom multi-agent systems.
• Design and optimize prompt engineering strategies, including few-shot learning, chain-of-thought, and tree-of-thoughts prompting.
• Build evaluation frameworks and benchmarks to measure model performance, accuracy, and safety.
• Collaborate with product teams to integrate AI capabilities into production applications with proper monitoring and safeguards.
• Optimize model inference performance through quantization, distillation, and efficient serving infrastructure.
• Contribute to the AI research community through publications, open-source projects, and conference presentations.

Requirements

• MS or PhD in Computer Science, Machine Learning, AI, or related field with focus on NLP or deep learning.
• Strong experience with PyTorch, TensorFlow, and Hugging Face Transformers library for training and deploying models.
• Deep understanding of Transformer architectures, attention mechanisms, and modern LLM techniques (GPT, BERT, T5, LLaMA).
• Hands-on experience with fine-tuning techniques including LoRA, prompt tuning, and instruction tuning.
• Proficiency in Python and experience with ML infrastructure tools (MLflow, Weights & Biases, Ray, Kubeflow).
• Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and semantic search technologies.
• Published research papers in top-tier conferences (NeurIPS, ICML, ACL, EMNLP) or significant open-source contributions.
• Strong mathematical foundation in statistics, linear algebra, and optimization theory.