Job Overview:

You’ll join the AI Platform team building and operating the cloud infrastructure, LLM gateway, and

serving layer behind our wealth management product. This is a hands-on ownership role on

Azure

Requirements:

• 3–5 years of platform / backend / infrastructure engineering, with real ownership of things running in production.
• Hands-on cloud experience with any major provider — Azure, AWS, GCP, or equivalent —including a solid grasp of core compute and networking (private networking, container/serverless compute, and how traffic flows between services).
• Infrastructure as code with Terraform, Bicep, or a comparable tool — modules, state, and the discipline of managing infra in code.
• Solid debugging instincts — you reach for logs, traces, and reproduction steps before guessing.
• Working knowledge of LLMs: what tokens, context windows, and quotas mean in practice, and how those constraints shape what you can build.
• Fluency with modern AI-assisted development tools — Claude Code, Cursor, Antigravity, OpenCode, or similar.

Nice to Have:

• Hands-on Azure specifically — VNets, subnets, private endpoints, Container Apps, Function Apps.
• System design experience, familiarity with AI infrastructure terms (tokenization, requests per minute, token budgeting)
• Strong Python, including async (asyncio), and a working grasp of concurrency, threading vs.async, and the pitfalls of each.
• Experience building or operating an LLM gateway or similar proxy/routing layer.
• Open-source model deployment and serving — running and hosting your own models (vLLM, Ollama, TGI, or similar), and the infra/optimization that comes with it.
• Exposure to LLM safety and governance — PII redaction, data classification, prompt/response guardrails.
• Cost optimization across cloud compute (plans, scaling, cold starts on serverless).
• Observability tooling (App Insights, OpenTelemetry, or similar)