Job Description & How to Apply Below
Design and implement enterprise-scale AI cloud architectures on AWS for GenAI, ML, and AI-enabled applications.
Define and standardize AI environment setup and lifecycle architecture across Sandbox, PoC/Dev, Pilot, and Production environments.
Architect secure multi-account AWS environments using AWS Organizations, Control Tower, IAM Identity Center, and centralized governance frameworks.
Define enterprise AI SDLC standards including environment promotion workflows, approval gates, deployment standards, auditability, and operational controls.
Design scalable and secure AI platform patterns leveraging AWS services such as Bedrock, ECS/EKS, Lambda, S3, API Gateway, Event Bridge, Cloud Watch, and related AI ecosystem services.
Establish AI governance guardrails including identity and access management, data classification controls, encryption standards, audit logging, secrets management, and compliance enforcement.
Design reusable architecture blueprints and reference implementations for AI/GenAI application onboarding.
Define high availability, fault-tolerant, scalable, and cost-optimized cloud architectures for AI workloads.
Collaborate with engineering teams, business stakeholders, security teams, and platform teams to align AI platform capabilities with enterprise requirements.
Lead Infrastructure as Code (IaC) and platform automation strategies using Terraform and cloud-native automation tooling.
Define observability, monitoring, logging, and operational support standards for AI workloads and platform services.
Establish AI cost governance, tagging strategies, usage monitoring, and optimization frameworks.
Evaluate and integrate emerging GenAI technologies, LLM frameworks, vector databases, and AI platform capabilities into enterprise architecture standards.
Provide technical leadership, mentoring, and architectural guidance to Dev Ops, platform engineering, and AI engineering teams.
Required
Skills & Experience:
8+ years of overall IT experience with strong focus on cloud architecture and enterprise platform engineering.
5+ years of hands-on experience architecting solutions on AWS cloud platforms.
Strong expertise in AWS services including:
Compute & Containers
EC2
Lambda
ECS/EKS
Auto Scaling
Storage & Data Services
S3
EBS
RDS
DynamoDB
Redshift
Networking & Security
VPC design
Transit Gateway
Security Groups
NACLs
Private Link/VPC Endpoints
IAM
KMS
Secrets Manager
AWS Organizations
Control Tower
Monitoring & Operations
Cloud Watch
Cloud Trail
Config
Guard Duty
Security Hub
Event Bridge
SNS/SQS
Managed Grafana
Strong understanding of enterprise AI/GenAI platform architectures and operationalization patterns.
Experience designing secure multi-account cloud environments and enterprise landing zones.
Hands-on experience with Infrastructure as Code (Terraform preferred).
Strong understanding of Dev Sec Ops and CI/CD implementation patterns.
Experience with containerization and orchestration technologies such as Docker and Kubernetes.
Experience implementing governance, auditability, and compliance controls in cloud-native environments.
Strong knowledge of API architecture, integration patterns, and distributed systems.
Experience working with Databricks and modern AI data platforms is preferred.
Strong stakeholder communication, architecture governance, and technical leadership capabilities.
Preferred Qualifications:
Experience building or supporting enterprise AI/GenAI platforms.
Exposure to AWS Bedrock, Sage Maker, vector databases, RAG architectures, or LLMOps frameworks.
Experience implementing AI governance and Responsible AI controls.
Familiarity with MLflow, Kubeflow, Lang Chain, or similar AI orchestration frameworks.
Experience with policy-as-code and cloud governance automation.
AWS Certifications such as:
AWS Certified Solutions Architect – Professional
AWS Security Specialty
AWS Dev Ops Engineer Professional
Experience in regulated enterprise environments with strong compliance and audit requirements.
Experience with hybrid cloud or multi-cloud architectures is a plus.
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