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 Solid 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 Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, Electronics, Mechanical Engineering, Mathematics, Statistics or a related discipline. Master’s 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.

📌 Senior Data Scientist (Yelahanka)
🏢 Cyient
📍 Yelahanka