Senior Platform Engineer
$94,900–$176,300 year
HybridToronto, Ontario, Canada or Ann Arbor, Michigan, United States
Job Summary
Design and deliver backend services powering AI agent workflows, including request ingestion, orchestration, and document handling. Contribute to CI/CD and progressive-delivery infrastructure for safe, continuous deployment of product and internal tools. Build and maintain cloud infrastructure using Infrastructure-as-Code practices, making architectural decisions focused on scalability and reliability within microservices. Uphold high engineering standards through automated testing, observability, and code review while participating in on-call rotation to support customer-facing systems. Mentor junior engineers and collaborate with product and applied-AI teams to de-risk complex projects.
Required Qualifications
- 4+ years of professional software engineering experience
- experience designing, building, and operating backend systems in production
- Strong proficiency in Python (FastAPI or similar) or another backend language
- experience in distributed systems
- experience in microservices
- cloud-native development
- Hands-on expertise with relational databases (PostgreSQL)
- caching/messaging systems (Redis)
- API design
- AWS
- container orchestration with Kubernetes (EKS)
- Experience with CI/CD pipeline engineering
- progressive/safe delivery patterns (e.g. staged rollouts, traffic-shifted or environment-based release strategies)
- Infrastructure-as-Code practices for managing cloud infrastructure
- Hands-on experience with observability tooling (e.g. OpenTelemetry, Datadog)
- Strong system design skills
- debugging skills
- performance optimization skills
- ability to make thoughtful trade-offs between speed, quality, and long-term scalability
- Participate in an on-call rotation to support our customer-facing systems and critical internal tooling
Desired Qualifications
- Experience operating systems built on or integrating with LLM/Generative AI infrastructure (e.g. managed agent runtimes, LLM gateways, RAG or document-retrieval pipelines)
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