Sr. Applied Intelligence Architect

The group you
16ll be a part of

The Enterprise AI team within Lam
16s Office of the CTO helps the company apply machine intelligence responsibly across enterprise operations, engineering workflows, software and controls, and product experiences. The team develops shared AI platforms, intelligence architecture, knowledge and ontology services, developer capabilities, evaluation, governance, and adoption patterns so Lam teams can create secure, reusable AI solutions that improve as model capabilities advance.

As 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. You will convert rapidly advancing AI research into deployed capabilities that produce measurable business, engineering, and product outcomes.

You will help ensure models remain replaceable, capabilities are composable and machine-consumable, actions are governed, and production outcomes strengthen future intelligence cycles. Your work will help Lam preserve technology choice, reduce vendor dependency, control cost, and build solutions that gain value as machine intelligence advances.

• Own Lam
  16s applied intelligence architecture and implementation roadmap across frontier, daily-driver, commercial, open-weight, fine-tuned, specialized, and physics or simulation-based models.
• Translate business and technical requirements into deployable intelligence solutions, including model selection and routing, context and retrieval, tool use, agent workflows, memory, integration, inference, and deployment patterns.
• Design model-agnostic interfaces, reusable contracts, and abstraction layers that allow platforms and products to adopt stronger intelligence without major redesign or unnecessary 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 over time.
• Embed governance by design through identity and scope, authority boundaries, evidence and provenance, auditability, human oversight, secure data handling, and reversible machine actions.
• Connect intelligence with enterprise knowledge, software tools, APIs, simulation, optimization, digital twins, and domain workflows through secure, typed, machine-consumable services.
• Lead practical experiments, lighthouse programs, architecture reviews, and production transitions; convert successful work into reference architectures, evaluation assets, and reusable engineering patterns.
• Partner with software, controls, product, data, security, legal, infrastructure, and domain teams to integrate appropriate intelligence into enterprise processes and Lam products.
• Maintain state-of-the-art awareness through industry, vendor, open-source, startup, and university engagement; translate relevant advances into recommendations, benchmarks, experiments, and technical decisions.
• Mentor architects and engineers, strengthen applied AI practices across Lam, and communicate complex model and architecture trade-offs to technical and executive audiences.

• Bachelor
  16s degree with 12+ years of relevant experience; or master
  16s degree with 8+ years; or PhD with 5+ years; or equivalent practical experience.
• Strong communication, technical leadership, and cross-functional collaboration skills, including the ability to influence senior technical and business stakeholders.
• 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…