Senior DevOps Engineer

Job Overview:You’ll join the AI Platform team building and operating the cloud infrastructure, LLM gateway, andserving layer behind our wealth management product. This is a hands-on ownership role on Azure for an engineer who can take infrastructure from code to production and keep it running reliably and cost-effectively.

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)