I am building an end-to-end pipeline that ingests millions of legal filings and returns clean, structured data ready for downstream analytics. Your focus will be to design, train, optimise and productionise state-of-the-art language models that reliably identify every relevant detail buried in those texts. The core task is named-entity recognition on complex legal prose. Models must capture the BIO boundaries and output rich entity labels while remaining performant at scale. I expect you to work comfortably with transformers, LLMs and RAG-style retrieval, refining them through techniques such as distillation, quantisation and pruning so inference stays fast and cost-effective. Entities to extract • Names and addresses • Legal terms and clauses • Dates and case numbers Alongside model work, you will own the MLOps path: packaging the model with Python and PyTorch, serving it through SageMaker (or an equivalent on AWS EC2), storing artefacts in S3, and wiring up Prometheus + Grafana for latency and accuracy dashboards. The entire solution has to cope with high-volume usage without sacrificing reliability or budget. Deliverables 1. A production-ready NER model fine-tuned on my legal dataset, with versioned checkpoints and scripts. 2. A scalable inference service (REST or gRPC) behind an autoscaling endpoint, complete with CI/CD. 3. Real-time monitoring and alerting dashboards for throughput, memory, precision/recall and cost. 4. Clear documentation of the data schema, training pipeline and upgrade path. If you have a track record with HuggingFace, spaCy or Flair, and you enjoy squeezing every last millisecond from large models while keeping deployment smooth, let’s talk.