Senior Applied Intelligence Architect

What you'll do

• Own the applied intelligence architecture and implementation roadmap for various model types across the enterprise.
• Translate business requirements into deployable solutions including model routing, retrieval, agent workflows, and inference patterns.
• Design model-agnostic interfaces and abstraction layers to prevent vendor lock-in and ensure platform scalability.
• Define evaluation systems to measure grounded correctness, reliability, safety, latency, cost, and business outcomes.
• Create reusable patterns for fine-tuning, quantization, distillation, synthetic data, and inference optimization.
• Architect closed-loop learning systems to capture operational signals and improve models and workflows over time.
• Embed governance by design through identity, auditability, secure data handling, and human oversight.
• Lead practical experiments and lighthouse programs to convert successful work into reference architectures and engineering patterns.

What we're looking for

• Bachelor’s degree with 12+ years of relevant experience; or master’s degree with 8+ years; or PhD with 5+ years; or equivalent practical experience.
• Substantial experience in applied AI or machine learning systems, AI architecture, applied research, distributed systems, cloud or platform engineering, or advanced software and product engineering.
• Demonstrated success designing and deploying production AI systems using foundation models, multimodal models, retrieval, tools, agents, or other learning-driven capabilities.
• Deep understanding of the model lifecycle, including evaluation, selection, post-training, fine-tuning, inference, observability, safety, cost, and continuous improvement.
• Strong hands-on technical capability with Python and modern AI or machine learning frameworks.
• Experience creating scalable architectures across APIs, services, event-driven systems, data platforms, containers, cloud infrastructure, and secure enterprise integration patterns.
• Experience deploying and optimizing open-weight models, including LoRA or adapter tuning, quantization, distillation, model serving, GPU utilization, and inference optimization (preferred).
• Experience connecting AI with physics engines, optimization, simulation, digital twins, controls, scientific or engineering tools, or hardware-software development workflows (preferred).