AI Engineer

We want hands-on AI Engineers and Scientists who possess an in-depth, systems-level understanding of advanced AI methods. Candidates must have deep technical expertise across one or more of the following core frontier domains:

• Graph Data Science & GNNs: Expertise in Graph Neural Networks (GNNs), graph data science, knowledge graphs, ontology, semantics, and graph-based reasoning or representation learning.
• Agentic AI: Demonstrated experience in agentic system design, autonomous workflow orchestration, agent-to-agent communication frameworks, and multi-agent architectures.
• NLP & LLM Infrastructure: Deep knowledge of large language model architecture, custom fine-tuning, quantization, model evaluation, and multi-modal or retrieval-augmented generation (RAG) pipelines.
• Machine Learning & Deep Learning: Hands-on experience with ML/DL model development and deployment at scale, including GPU computing, cluster infrastructure, and hardware optimization.
• Generative Systems: Advanced familiarity with GANs, VAEs, and diffusion-based generative architectures.

Education & Experience

• MS or PhD in Computer Science, Artificial Intelligence, Data Science, or a highly quantitative field is strongly preferred.
• Production Experience: A proven track record of building, optimizing, and shipping production-grade AI systems at scale (not just localized scripts or theoretical models).
• Systems-Level Depth: In-depth knowledge of underlying systems, algorithms, and frameworks-candidates must understand how the models function under the hood. #LI-JD1 #LI-Remote

We are sourcing elite engineering and scientific talent for the dedicated GenAI Practice of a leading Big 4 consulting firm. This team is embedded across multiple lines of service, building and deploying enterprise-scale, AI-powered products at the absolute frontier of generative AI, graph science, and agentic systems.