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Mid-level generalist title with AI specialization yields moderate applicant density and competition.
Strong ML/LLM production and AI-native tooling requirements make cross-industry fit limited and domain-sensitive.
Explicit years range plus mandatory AI/LLM, Python, cloud, and production experience creates strict screening filters.
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Design, build, and operate production AI backend services, APIs, and platform components with LLM-powered features.
Develop and maintain AI-native systems including RAG pipelines, AI SDKs, evaluation workflows, guardrails, and prompt/tool orchestration for institutional financial clients.
Drive technical excellence through design reviews, reduce technical debt, and improve reliability, scalability, and operational quality of AI systems.
5+ years of experience designing, building, and operating production software systems.
Strong backend engineering experience with Python frameworks (FastAPI, Flask, or Django).
Experience building or integrating AI/LLM-powered production systems (e.g., RAG pipelines, AI SDKs, evaluation workflows).
Proficiency with AWS, Docker, Kubernetes, and cloud-native technologies.
Experienced in AI-native development workflows with hands-on use of AI tools like Cursor and Augment to accelerate development.
Skilled at critically evaluating AI-generated code including identification of failure modes and regressions.
Background in backend system design with familiarity or interest in fintech/financial services domain or document processing pipelines (preferred but not mandatory).