AI/ML Platform Architect
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About the Role
Aerospace is at a turning point — costs are rising, supply chains are strained, and old ways of building can’t keep up. At Neon Aero, we’re breaking that mold. We move fast, think big, and combine start-up agility with aerospace expertise to design, build, and scale aircraft in ways the industry has never seen. Our team leverages automation, AI-driven tools, and a fully connected digital backbone to accelerate innovation and reduce barriers to production.
Here, you won’t just take a job — you’ll help reinvent how the world flies. If you’re ready to solve complex problems, push technology further, and work alongside some of the brightest minds in aerospace, this is your chance to make a real impact.
Neon is building the next generation of aerospace systems as a Tier-1 supplier—AI-native, digitally integrated, and compliance-ready from day one. We’re looking for an AI/ML Platform Architect to own our AI enablement layer end-to-end: the platform, standards, and production patterns that let teams ship classic ML and generative AI with speed and rigor. This is a high-impact, hands-on role where you’ll establish Neon’s MLOps + LLMOps foundation, deliver reference implementations that unlock rapid delivery, and—over time—build and deploy Neon-owned models, agents, and AI applications.
Why This Role Matters:
In many companies, AI experimentation outpaces operational discipline. At Neon, AI must be both powerful and auditable. Without a strong platform architecture, AI systems become fragmented, inconsistent, and risky—especially in an aerospace context where traceability and governance aren’t optional. This role ensures AI is built on a controlled, production-grade foundation so it becomes a scalable capability embedded across Neon’s digital backbone—without sacrificing security, compliance, or first-pass quality.
You will get to:
- Build Neon’s AI/ML platform: training workflows, model registry, deployment frameworks, and monitoring standards
- Define and operate LLMOps capabilities: prompt management, evaluation harnesses, RAG patterns, and agent runtime standards
- Own retrieval + vector architecture: embedding strategy, indexing standards, latency targets, and cost controls
- Design model serving and deployment patterns for mixed environments (managed services and self-hosted) based on best fit
- Establish CI/CD for models and AI services, including automated evaluation gates, safe releases, rollbacks, and change control
- Implement runtime observability for models, agents, and retrieval systems (quality, drift, safety signals, and cost)
- Define AI governance: model cards, evaluation criteria, approval workflows, audit logging, and versioning for models/prompts/agents
- Partner with IT/security to define controls—while remaining accountable for AI security outcomes and requirements
- Ensure data access compliance, honoring data platform constraints and “data always wins” tie-break rules
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