Private Cloud AI Architect & Agentic Systems Leader

Jobtailor

California (MO)

On-site

USD 180,000 - 260,000

Full time

5 days ago

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Job summary

Jobtailor seeks a senior Lead AI Architect to steer architecture across private and hybrid cloud deployments. You will design scalable Agentic AI and Generative AI solutions, mentor teams, and drive technical decisions for PCAI products and customer solutions.

In this role, you will lead architecture workshops, evaluate partner technologies, and define blueprints for AI security, governance, and observability, while shaping roadmaps and TCO analyses for strategic AI initiatives.

Qualifications

Bachelor's degree in CS or related field.
15+ years of technical leadership and architecture experience.
2+ years designing scaled Agentic AI and Generative AI in production.
2+ years designing agentic AI platforms used by multiple solutions/teams.
5+ years building large-scale AI/ML on at least one major cloud.
5+ years deep knowledge of AI tech from cloud providers/open-source/vendors.
Hands-on with foundation models and LLMs.
Experience with RAG pipelines, multi-agent systems, tool-using agents.
Knowledge graphs and semantic layers experience.
AI security/governance/observability in production.
Deep learning architectures incl. transformers/sequence models.
Pre-training, fine-tuning, distillation, and model alignment.
Cloud-native architectures: containers, microservices, Kubernetes, service meshes.
Proven track record deploying mission-critical, distributed platforms.
Ability to lead through influence and drive decisions.
Excellent communication across executives, engineers, customers.

Responsibilities

Lead AI architecture and technical design workshops with internal teams and customers.
Define customer-ready AI architectures for private and hybrid cloud.
Evaluate and select ISV partner technologies for AI platform stack.
Develop TCO and roadmap options and drive architectural decisions for PCAI products.
Design multi-agent, LLMOps/AgentOps, and AI security/governance blueprints.
Set architecture direction for AgentOps/LLMOps, AI Governance, AI Security, and AI Observability capabilities.
Build reusable AI components and agents with engineering to productionize them.
Troubleshoot and optimize AI systems at scale.
Establish best practices for model lifecycle, evaluation, and responsible AI.
Recommend design optimizations for performance, cost, reliability, and trustworthiness.
Create and present technical content including reference architectures, whitepapers, talks, and publications.
Mentor senior engineers and architects; provide technical leadership across orgs.