6+ years of progressive engineering experience, including 1-2+ years in AI platform, cloud platform, or emerging-tech enablement roles.
Demonstrated experience working with InfoSec, Risk, and Compliance teams to define, review, and implement technical controls in regulated environments.
Hands-on experience implementing controls through configuration and code: IAM/access policies, guardrails, logging and audit trails, quota/rate limiting, and network/data-protection controls.
Gen AI models (GPT, Claude, Gemini, LLaMA) and prompt engineering techniques.
Agentic AI, MCP, and Graph/RAG architectures, including building and supporting agents, skills, and MCP servers.
Gen AI frameworks and LLM gateway/proxy patterns.
AWS cloud services (AgentCore, Bedrock, EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formation), including cost management and usage/credit monitoring.
Infrastructure as Code (Terraform, Puppet, Docker) and containerized deployments.
Python programming (NumPy, Pandas, Boto3) for automation, tooling, and platform services.
Vector/Graph databases (Weaviate, Milvus, PGVector, Neo4j, Neptune) and query optimization.
Automated testing and evaluation frameworks (Ragas, Playwright, Selenium, Zephyr).
Familiarity with SDLC best practices, DevSecOps, Agile Scrum/Kanban, and work management tools (JIRA, Confluence, JIRA Align).
Strong stakeholder management and ability to broker agreements across security, risk, compliance, and engineering teams.
Clear written and verbal communication, including translating technical controls into business language and vice versa.
Customer-service mindset for user support, with the ability to triage, prioritize, and resolve technical issues efficiently.