Senior Data Scientist

Role Summary

We are hiring a hands-on Senior Data Scientist / AI Engineer to design and productionize AI capabilities for automated understanding and validation of complex 2D engineering drawings. The role combines Computer Vision, OCR/Document AI, multimodal LLMs, RAG/Graph-RAG, Knowledge Graphs and deterministic engineering rules.

Key Responsibilities

• Build AI pipelines to extract title blocks, notes, dimensions, tolerances, GD&T, datums, callouts, tables, references and drawing views from PDF/DWG/DXF-derived engineering content.

• Develop Computer Vision models for region detection, layout understanding, object detection, segmentation, symbol recognition, view classification and annotation localization.

• Design OCR/document-processing pipelines for dense technical drawings using tools such as PaddleOCR, Docling, OpenCV, YOLO, Vision Transformers or equivalent frameworks.

• Build multimodal/LLM workflows for engineering interpretation, structured extraction, contextual reasoning and JSON-based outputs with strong guardrails.

• Develop RAG and Graph-RAG solutions over engineering standards, specifications, component rules and business-unit guidelines using vector search and Knowledge Graphs such as Neo4j.

• Implement hybrid validation combining deterministic rule engines, symbolic checks and LLM reasoning; ensure exact geometric/tolerance checks are not delegated solely to LLMs.

• Create explainable review findings including violated rule, source/citation, confidence, rationale and recommended corrective action.

• Design Human-in-the-Loop feedback mechanisms to capture accept/reject/edit/override decisions and improve models, prompts, retrieval and rules.

Model Evaluation & Productionization

• Define accuracy benchmarks and golden datasets; track precision, recall, F1, IoU, OCR CER/WER, extraction accuracy, retrieval quality, validation accuracy and hallucination rate.

• Perform failure analysis, prompt/model benchmarking, regression testing and continuous accuracy improvement.

• Convert experiments into production-grade Python/FastAPI services; containerize with Docker and support Kubernetes/GPU deployment.

• Optimize inference latency, GPU memory and throughput for enterprise NVIDIA infrastructure such as H100/A100 or equivalent.

• Implement MLOps practices for model/dataset/prompt versioning, experiment tracking, model registry, deployment pipelines and monitoring.

Required Technical Skills

Area

Expected Capability

Programming

Python, SQL; strong software engineering practices

Computer Vision

OpenCV, YOLO/object detection, segmentation, layout understanding, image preprocessing

OCR / Document AI

Paddle OCR, Docling or equivalent; table/text/technical-document extraction

Deep Learning

PyTorch and/or TensorFlow; transfer learning and model evaluation

GenAI / Multimodal

LLMs, VLMs, prompt engineering, structured outputs, tool/function calling

RAG / Graph-RAG

Embeddings, chunking, vector search, hybrid retrieval, reranking, query transformation

Knowledge Graph

Neo4j or equivalent graph DB; graph modeling and graph retrieval

LLM Optimization

LoRA/QLoRA, PEFT, quantization, fine-tuning, context and inference optimization

AI Engineering

FastAPI/REST, Docker, Kubernetes, Git, GPU deployment

MLOps

MLflow or equivalent; experiment/model lifecycle, monitoring and CI/CD integration

Preferred Domain Experience

Strong preference for candidates with experience in aerospace, mechanical/manufacturing, automotive, rail, CAD/PLM, engineering drawing automation or technical document intelligence. Exposure to GD&T, ASME Y14.x, ISO GPS or equivalent drawing standards is highly desirable.

Education

Bachelors or Masters degree in Computer Science, AI/ML, Data Science, Electronics, Mechanical Engineering, Mathematics, Statistics or a related discipline. Masters degree is preferred.

Candidate Profile

• Hands-on senior engineer who can code, train/evaluate models, troubleshoot accuracy issues and take AI components into production.

• Able to work closely with engineering SMEs and convert standards/domain rules into machine-readable knowledge and validation logic.

• Comfortable operating in secure enterprise environments with restricted internet access and on-premises model hosting.