Sr. Applied Intelligence Architect - US-CA-Fremont (1003), United States

Originally posted 13 August 2026 by the employer.

This role focuses on architecting and implementing Lam’s applied intelligence architecture across various model types.

About the role

As a Senior Applied Intelligence Architect, you will establish how Lam selects, adapts, evaluates, routes, integrates, and governs commercial, open-weight, fine-tuned, specialized, and physics-informed intelligence to produce measurable business, engineering, and product outcomes. Your work will help Lam preserve technology choice, reduce vendor dependency, control cost, and build solutions that gain value as machine intelligence advances.

What you'll do

• Own Lam’s applied intelligence architecture and implementation roadmap across various model types.
• Translate business and technical requirements into deployable intelligence solutions, including model selection, routing, context, retrieval, tool use, agent workflows, memory, integration, inference, and deployment patterns.
• Design model-agnostic interfaces, reusable contracts, and abstraction layers to adopt stronger intelligence without major redesign or vendor lock-in.
• Define qualification and evaluation systems that measure grounded correctness, domain performance, reliability, safety, latency, throughput, cost, compute efficiency, reproducibility, and business outcomes.
• Create reusable patterns for fine-tuning, LoRA and adapters, distillation, synthetic data, prompt and context engineering, caching, quantization, inference optimization, and open-weight deployment.
• Architect closed-loop learning systems that capture outcomes, failures, feedback, evidence, and operational signals to improve models, software, hardware requirements, and workflows.

What you'll need

• 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.

Nice to have

• Experience deploying and optimizing open-weight models, including LoRA or adapter tuning, quantization, distillation, model serving, GPU utilization, and inference optimization.
• Experience with multi-model routing, automated evaluation, agentic systems, reinforcement learning, long-horizon optimization, or self-improving engineering workflows.
• Experience connecting AI with physics engines, optimization, simulation, digital twins, controls, scientific or engineering tools, or hardware-software development workflows.
• Familiarity with enterprise AI and data platforms such as Azure AI Foundry, Kubernetes, MLflow, knowledge graphs, ontology platforms, and Microsoft Fabric.
• Experience applying responsible AI, secure software development, model governance, intellectual-property protection, and data controls in a large global or highly regulated enterprise.
• Semiconductor, industrial equipment, scientific computing, manufacturing, or complex cyber-physical product experience.
• Experience collaborating with universities, research organizations, open-source communities, startups, and strategic technology partners.

Skills: applied AI, machine learning systems, AI architecture, model lifecycle, inference optimization