AI Platform Engineer

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

• Infrastructure and Scaling: stand up and maintain the compute environment AI workloads run on, including containerized and GPU-backed resources, so workloads run efficiently and cost-effectively
• Automate safe rollouts: build automated pipelines for rolling out AI platform updates, with rollback built in
• Build retrieval systems: connect AI models to the organization's own data so responses are grounded in real, current information
• Maintain vector stores: connect and maintain the vector stores and data sources retrieval depends on
• Route and gate model access: give people one governed way to reach approved AI models and providers, routing each request to the model that actually fits the job
• Redact sensitive data: strip sensitive or private data out of prompts before they reach a model
• Build guardrails: put guardrails in place to catch harmful, biased, or simply wrong AI output before a user sees it
• Enable self-service without losing control: set up access and permissions so people across the organization can use approved AI capabilities on their own, while the platform still enforces where the line is
• Monitor system health: watch system health, response times, and error rates, and catch problems before people have to report them
• Track usage and spend: track usage and spend at the model and feature level so cost surprises do not happen
• Apply cost discipline: apply the same cost-management discipline to AI spend that the platform team applies everywhere else
• Build self-service tools: build self-service tools and APIs so other teams can ship AI features without needing to understand the plumbing underneath
• Report on AI performance: expose usage, spend, and performance metrics through APIs or dashboards so AI costs and performance are visible across the organization

• Platform engineering experience: 4+ years of hands‑on experience in platform engineering, cloud infrastructure, or Dev Ops
• AI in production: experience running AI or LLM platforms in production, including retrieval systems that ground model answers in real company data
• AI systems judgment: understands that AI systems behave differently from regular software, and knows how to test, evaluate, and monitor them so problems get caught early
• Programming and automation: strong Python skills, including building integrations against APIs
• Cloud and containers: solid experience with at least one major cloud provider (AWS or Azure) and building on containerized infrastructure
• Cost and model judgment: some sense of cost and performance tradeoffs between AI models, and when a smaller, cheaper model is the right call
• Reporting and transparency: experience building or exposing metrics through APIs or dashboards for reporting and visibility beyond the immediate team
• Security mindset: builds with security as a first‑class concern: least‑privilege access, careful handling of sensitive data, sensible defaults
• Self‑service thinking: understands how to make AI capability self‑service, and where the guardrails actually need to hold
• Full‑stack comfort: equally comfortable in infrastructure, automation, integration, and application code

• Terraform or similar infrastructure‑as‑code experience
• Exposure to cloud cost management or Fin Ops practices
• Deeper experience with retrieval pipelines: chunking, embeddings, indexing, and ranking, not just calling a vector database
• Experience routing requests across multiple AI model providers based on cost, speed, or capability
• Judgment about when a simple, predictable workflow beats an open‑ended AI agent, with experience building both
• Familiarity with container orchestration for AI or compute‑heavy workloads
• Familiarity with governing tool access…