Infrastructure Engineer (AI for Financial Services)

An early-stage company is building an enterprise AI platform for financial institutions, working with sensitive data under tight security and compliance rules. It is hiring infrastructure engineers to design and protect the systems underneath the product, including customer environments that run inside the client’s own cloud.

Scope

Design scalable infrastructure behind an AI knowledge engine that works over both structured feeds and unstructured internal documents

Apply a security-first approach to private cloud deployments so data governance satisfies financial-services expectations

Build dependable ingestion pipelines that combine public data with isolated, per-customer private data

Set up monitoring and alerting for the bring-your-own-cloud deployment model

Add permissions and audit logging so every AI answer can be traced back to its original sources

Collaborate with the research and product groups to tune infrastructure for LLM serving, training and agent workloads

Set up CI/CD and declarative infrastructure practices so releases ship fast, reliably and across several cloud vendors

Qualifications

At least five years in platform or infrastructure engineering; staff-level experience is preferred

Deep knowledge of one or more major clouds, together with containers and Kubernetes

A security mindset and experience with data governance, particularly in regulated sectors

Familiarity with monitoring and observability tooling (Datadog in use here)

Understanding of compliance frameworks such as SOC, SOX and GDPR; financial regulation knowledge helps

Startup instincts: moving quickly while still building robust systems

Exposure to managed AI services like SageMaker or Bedrock

On-site in the San Francisco Bay Area or New York, NY. Visa sponsorship and transfers considered.