Job Opportunities

Cloud AI Architect

Cloud & Infrastructure (Core — Non-Negotiable):- Hands-on AWS architecture —
EKS/ECS, VPC, IAM, S3, SQS/SNS, CloudWatch — has deployed, not just certified- Infrastruc
ture as Code — Terraform or AWS CDK, not click-ops- Container orchestration — Kubernetes or
ECS, auto-scaling (HPA/KEDA), service mesh- API gateway / reverse proxy design — routing,
rate limiting, authentication, audit logging- CI/CD pipelines — end-to-end, automated, not
manual deploys- Microservices architecture — event-driven, queue-based decoupling, independent
scaling- Networking and security — VPCs, security groups, encryption at rest/in transit, secrets
management- Cost management — FinOps thinking, right-sizing, spot vs on-demand decisions
AI Workload Awareness (Required — But Builds on Cloud Skills):- Understanding of
LLM API patterns — token-based billing, streaming responses, caching strategies, model routing- GPU instance management — for hosting open-source models or fine-tuning (instance types,
scheduling, utilization)- Familiarity with AI-specific tooling — model serving (Triton, vLLM, TGI),
vector databases, embedding pipelines- Understanding that AI outputs are non-deterministic —
monitoring and testing strategies differ from traditional APIs- Experience integrating open-source
libraries into production systems — OCR, NLP, document processing tools
Operational Mindset (Non-Negotiable):- Asks “what happens when this breaks?” before
“what’s the accuracy?”- Thinks about monitoring, alerting, and reliability as first-class concerns
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- Designs for operability — the best system is the one that can be operated, maintained, and
debugged at 2am- Comfortable in regulated environments — life sciences requires audit trails,
access controls, and data governance