AI Lead

Lead AI Engineer

About the Role:

We are seeking an experienced AI specialist with a strong Computer Science/Engineering background to design, develop, and deploy advanced Generative AI-based solutions. In this role, you will build intelligent AI agents, leverage graph-based RAG techniques, and ensure robust production deployments to solve complex challenges in the Architecture, Engineering, and Construction (AEC) domain.

Key Responsibilities

• Collaborate with stakeholders to align AI initiatives with business goals.
• Lead and mentor a team of ML engineers and data scientists.
• Design, develop, and deploy enterprise-grade RAG systems that deliver accurate, context-aware responses in production environments.
• Incorporate AI agents and graph-based techniques (e.g., GraphRAG) to enable enhanced contextual data retrieval and support complex query relationships.
• Implement robust evaluation frameworks to measure and enhance RAG system effectiveness.
• Create scalable pipelines for document processing, embedding generation, and knowledge base management.
• Build monitoring systems to track performance, detect issues, and ensure RAG system reliability.
• Architect cloud-native solutions that can scale to handle enterprise document volumes and query loads.
• Explore and integrate cutting-edge techniques in Generative AI, including but not limited to model fine-tuning.

Qualifications & Skills

• A strong foundation in Computer Science or Engineering with 5-10 years' experience in NLP, Computer Vision, Deep Learning, Machine Learning, or related field.
• Extensive experience with LLMs and related technologies (e.g., embedding models, vector databases, etc.).
• Proven expertise in developing production-grade RAG systems.
• Knowledge of graph-based RAG techniques (e.g., Graph RAG or alternatives).
• Proficiency in Python and familiarity with Generative AI frameworks such as Hugging Face, Lang Chain, Llama Index, Prompt flow, Auto Gen, etc.
• Hands-on experience with cloud-based AI deployments (Azure preferred).
• Experience in LLM fine-tuning and optimization (e.g., LoRA) is a plus.
• Exposure to multi-modal RAG systems and LLMOps frameworks.
• Familiarity with deep learning architectures (e.g., Transformers, CNNs, GANs) and modern ML frameworks (e.g., PyTorch, Tensorflow, etc.).