Required Education:
Bachelor's Degree Bachelor's degree in Computer Science, Engineering, Information Technology, or a related quantitative field, or an equivalent combination of education and experience.
Required Experience:
3+ years of progressive experience in platform engineering, cloud engineering, DevOps/SRE, software engineering, or related infrastructure engineering roles, with direct experience operating production cloud services at scale.
Preferred Experience:
3+ years Experience configuring and managing Portkey or comparable enterprise AI gateways and LLM proxy systems. Familiarity with specialized AI observability and evaluation frameworks (e.g., LangSmith, Phoenix, Arize, Traceloop). Experience with FinOps practices applied specifically to token-based consumption and AI workloads. Experience delivering and supporting infrastructure services in a large, federated, higher-education, research institution, or similarly decentralized enterprise environment.
Required Skills, Knowledge and Abilities:
Cloud Infrastructure: Deep experience with AWS cloud services and cloud-native architecture (experience with AWS Bedrock is highly desirable). Infrastructure-as-Code: Demonstrated proficiency with Terraform, AWS CloudFormation, or equivalent IaC tools. Containers & Orchestration: Hands-on experience with Docker and Kubernetes (EKS) or AWS ECS. Automation & CI/CD: Hands-on experience building and supporting CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins) and modern source control strategies. APIs & Security: Strong foundation in API design, gateway management, authentication mechanisms (OAuth2, OIDC), service-to-service communication, IAM, secrets management (e.g., AWS Secrets Manager, HashiCorp Vault), and cloud network security. Observability: Practical experience setting up metrics, logging, distributed tracing, and alerting tools (e.g., Prometheus, Grafana, Datadog, AWS CloudWatch, OpenTelemetry). Programming Languages: Proficiency in Python and/or modern programming languages used for automation and platform tooling. GenAI / LLM Domain Expertise: Practical working knowledge of LLM APIs, foundation models, token-based usage models, model routing/gateways, streaming responses, rate limiting, and the operational constraints/latency characteristics of Generative AI services.