AI Infrastructure Architect at Oracle Risk Management Services

, , -

Full Time

Start Date

Immediate

Expiry Date

24 Apr, 26

Salary

0.0

Posted On

24 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Infrastructure, ML Platforms, Network Topologies, Security Controls, Disaster Recovery, Observability, GPU Infrastructure, Kubernetes, Cloud Solutions, Data Governance, Identity Management, Incident Response, Performance Optimization, Consulting Leadership, Stakeholder Management, Mentoring

Industry

IT Services and IT Consulting

Description

What you will do (Key responsibilities) 1) Architect and deliver customer AI infrastructure (end-to-end) Lead architecture and implementation for secure, scalable AI/ML/LLM platforms based on customer requirements and constraints. Produce implementation-ready artifacts: HLD/LLD, reference architectures, network/topology diagrams, deployment plans, runbooks, and operational handover packs. Translate business and technical requirements into a scalable target state, and guide delivery teams through build, rollout, and production readiness. 2) Solve real enterprise constraints (network + access + topology) Design enterprise network topologies with segmentation/isolation: private subnets, route tables, security policies, egress control, private endpoints, controlled ingress patterns. Work within common enterprise constraints Fixed network address plans (pre-approved CIDR ranges), IP allowlists/deny-lists, and limited routing flexibility Private connectivity requirements (VPN/Direct Connect/FastConnect/ExpressRoute), no public endpoints, and restricted DNS resolution Controlled administrative access (bastion/jump host, privileged access management, session recording, time-bound access) Restricted egress (proxy-only outbound, firewall-controlled destinations, egress allowlists, DNS filtering, no direct internet)Ensure secure data movement and integration patterns for AI workloads (east-west and north-south traffic) Customer-managed encryption and key custody (KMS/HSM, BYOK/HYOK, key rotation, certificate lifecycle) Strict TLS policies (mTLS, approved ciphers, enterprise PKI, certificate pinning where required) Identity and access controls (SSO/SAML/OIDC, RBAC/ABAC, least privilege, break-glass accounts) Data governance constraints (PII/PHI handling, residency/sovereignty, retention, audit evidence requirements) Secure software supply chain (approved base images, artifact signing, SBOM, vulnerability scanning, patch SLAs) Endpoint controls (EDR agents, OS hardening standards, restricted packages, golden images) Change management gates (CAB approvals, limited maintenance windows, separation of duties) Observability restrictions (logs can’t leave tenant, redaction/masking, approved collectors/forwarders only) Multi-tenant isolation and policy boundaries (namespace isolation, network policies, runtime sandboxing) High availability & DR expectations (multi-zone patterns, backup/restore, failover runbooks, RTO/RPO) 3) Security-by-design, InfoSec approvals, and guardrails for AI platforms Lead InfoSec engagement: threat modeling, control mapping, evidence collection, remediation plans, and security signoffs for AI infrastructure. Implement security controls and platform guardrails: TLS/SSL-only communication patterns; encryption-in-transit and encryption-at-rest API security: OAuth2/JWT/mTLS, gateway policies, request signing patterns where required Secrets management using vault/key management services, rotation and lifecycle controls IAM and least-privilege access models; tenant/project isolation VM hardening (CIS-aligned baselines), patching strategy, secure images “Kill switches” / emergency stop mechanisms for agents (tool-disable, egress cut-off, policy stop, rollback runbooks) AI infra guardrails: controlled tool execution, outbound allowlists, boundary policies, audit-ready logging 4) LLM hosting, GPU infrastructure, and scale Architect LLM hosting patterns: managed endpoints, self-hosted inference, multi-model routing, and workload isolation. Design and operationalize GPU-based inference at scale: Capacity planning, GPU node pools, scaling policies, cost/performance optimization Performance profiling and reliability patterns for inference services Build container/Kubernetes-based AI platforms (OKE/EKS/AKS/GKE as applicable): Secure cluster designs, namespaces/tenancy, node isolation, secrets, and safe rollout strategies Support AI frameworks and application runtimes on Kubernetes for scale and portability 5) Observability, reliability engineering, and operational readiness Define and implement observability across AI systems: Metrics, logs, traces, audit trails, and network call tracing Integration with enterprise observability tools (customer standard platforms) Define SLIs/SLOs for AI services: Latency, throughput, error rates, saturation, GPU utilization, queue depth, retry behavior Execute load testing and capacity validation for inference endpoints, vector stores, agent runtimes, and integration services. Build reliable ops workflows: incident response, runbooks, dashboards, alerting, and proactive health checks. 6) Disaster recovery and resilience for AI platforms Design DR strategies for AI solutions: Multi-AD / multi-region patterns, backup/restore for critical stores, IaC-based rebuilds Failover runbooks, RTO/RPO alignment, and validation exercises Ensure production-grade resilience and safe rollback for platform and application layers. 7) Red teaming and risk mitigation for AI infrastructure Drive security validation for AI infrastructure and agent deployments: Attack surface review, secrets leakage paths, egress abuse scenarios Prompt/tool misuse impact assessment at infrastructure level Implement mitigations and hardening measures with measurable controls. 8) Consulting leadership and stakeholder management Act as a trusted technical advisor to customer platform, network, and security teams. Communicate clearly with diverse stakeholders (CIO/CTO, Security, Infra, App teams) and drive decisions under ambiguity. Mentor engineers/architects, conduct design reviews, and build reusable delivery accelerators and blueprints. As a world leader in cloud solutions, Oracle uses tomorrow’s technology to tackle today’s challenges. We’ve partnered with industry-leaders in almost every sector—and continue to thrive after 40+ years of change by operating with integrity. We know that true innovation starts when everyone is empowered to contribute. That’s why we’re committed to growing an inclusive workforce that promotes opportunities for all. Oracle careers open the door to global opportunities where work-life balance flourishes. We offer competitive benefits based on parity and consistency and support our people with flexible medical, life insurance, and retirement options. We also encourage employees to give back to their communities through our volunteer programs. We’re committed to including people with disabilities at all stages of the employment process. If you require accessibility assistance or accommodation for a disability at any point, let us know by emailing accommodation-request_mb@oracle.com or by calling +1 888 404 2494 in the United States. Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

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Responsibilities

The AI Infrastructure Architect will architect and deliver end-to-end AI infrastructure solutions tailored to customer needs. They will also lead security engagements and ensure operational readiness for AI platforms.