Oracle
AI Technical Architect
Job Description
This manager-level position is responsible for people management and sales enablement. May also be responsible for project oversight of staff augmentation projects.
Required experience and qualifications
- 15+ years of experience in infrastructure architecture, cloud engineering, or platform consulting, with proven ownership of end-to-end architecture and delivery.
- Strong fundamentals in networking, operating systems, distributed systems, and enterprise security.
- Proven experience delivering secure, highly available platforms in regulated or enterprise environments.
- Deep hands-on experience with:
- Cloud infrastructure (OCI preferred; AWS/Azure/GCP acceptable)
- Enterprise network design (VPC/VCN, VPNs, routing, firewalls, proxies, private endpoints, DNS)
- Kubernetes/container platforms (OKE/EKS/AKS/GKE), secure cluster patterns, and scaling strategies
- Infrastructure-as-Code (Terraform strongly preferred) and automation (Python/shell)
- Observability stacks (logs/metrics/traces) and integration with enterprise monitoring tools
- IAM, vault/key management, secrets handling, encryption standards, and audit controls
- Strong customer-facing skills: requirements discovery, architecture documentation, and delivery leadership.
Preferred (nice-to-have) skills
- LLM inference serving (open models and/or managed endpoints), multi-model routing, and AI workload isolation.
- GPU platform engineering: scheduling, node pool design, performance tuning, and cost controls.
- Experience implementing agentic AI runtime patterns with safe tool execution and enterprise guardrails.
- Hybrid and multi-cloud deployments, including on-prem connectivity and enterprise integration patterns.
- Familiarity with data platforms relevant to AI (vector stores, metadata stores, object storage patterns).
Core competencies (what we value)
- Systems thinking and security-first architecture mindset
- Strong problem solving in constrained enterprise environments
- Crisp documentation and executive-ready communication
- Hands-on delivery orientation (not just advisory)
- Ownership, urgency, and accountability for production outcomes
Scope and impact (IC4/M3 expectations)
- Independently leads complex customer AI infrastructure programs from discovery through production and handover.
- Unblocks security/network constraints and drives approvals with clear evidence and mitigations.
- Establishes reusable, referenceable blueprints (secure AI landing zones, LLM hosting patterns, DR templates, observability baselines).
- Raises the quality bar by mentoring teams and institutionalizing guardrails, reliability practices, and delivery accelerators.
- Excellent leadership, communication, and stakeholder management skills.
- Manage a small team of AI consultants
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