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Tier-1 startup, generalist backend title, metro location and broad stack requirements increase candidate competition.
Core backend skills transfer across industries, but AI inference, Temporal, and OCR experience add domain specificity.
Explicit 1–2 years plus mandatory production backend skills and specific stack requirements increase filtering rigor.
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Build and own backend services within the document intelligence harness, managing ingestion, inference, post-processing, validation, and assembly of multi-page document data into structured JSON.
Develop and maintain REST APIs and Temporal workflows to support both synchronous and asynchronous document processing pipelines that must be durable, observable, and performant.
Instrument and debug production systems end-to-end, including monitoring latency and costs per stage, addressing workflow issues, and collaborating closely with the models team to optimize model integration.
1–2 years of professional experience building backend services used in production.
Strong proficiency in Go or Python, including knowledge of clean, tested, and concurrent code under load.
Solid understanding of HTTP/REST API design and asynchronous/background job processing.
Familiarity with PostgreSQL, Redis, Docker, and basic Kubernetes exposure.
Experience working with complex data processing pipelines or document intelligence systems in a production environment.
Comfortable debugging using logs, traces, and code rather than guesswork, indicating strong engineering rigor.
Genuine interest in AI systems engineering with a desire to deeply integrate and optimize AI/ML models in production systems.