Academic AI
Production evaluation pipeline with rubric-aligned generative assessment and self-consistency verification. Retrieval-augmented feedback synthesis across configurable assessment criteria. 500+ documents per evaluation cycle, 3 institutional deployments, 60% overhead reduction.
Production evaluation pipeline deployed across 3 institutional targets, processing 500+ documents per cycle. The architecture addresses four core challenges: maintaining assessment consistency, grounding generative feedback in configurable rubric criteria, balancing throughput with granularity, and providing human-in-the-loop override without disrupting throughput.
High-throughput evaluation environments face a fundamental scaling constraint. Manual assessment at scale results in delayed feedback cycles, inconsistent evaluation across parallel tracks, and reduced granularity under time pressure.
The system addresses the consistency-throughput tradeoff through rubric-aligned generation with self-consistency verification at the output boundary. Uncertain evaluations are flagged for human review. Configurable assessment criteria allow adaptation across deployment targets without architectural changes.
Applied AI Engineer
Build and extend RAG evaluation pipelines with self-consistency verification. Optimize rubric-aligned generation and feedback quality at scale.
Apply →Backend Engineer
Design API layers, document ingestion, async evaluation queues, and instructor override workflows.
Apply →Frontend Engineer
Build evaluation dashboards with real-time feedback rendering and clear UX for instructors.
Apply →Data Engineer
Build document processing from raw upload to structured representation for the evaluation pipeline.
Apply →AI Researcher
Benchmark rubric-aligned RAG and self-consistency; design evaluation datasets, ablation studies, and methodology for institutional deployments.
Apply →DevOps / MLOps
CI/CD, deployment pipelines, and monitoring for FastAPI, Celery, Redis, and production APIs.
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