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About Teleradiology Solutions / Daignostix Teleradiology Solutions (TRS), founded in 2002 by two Yale-trained physicians and headquartered in Ardmore, PA, provides 24x7 diagnostic radiology reporting to over 150 hospitals across 21 countries, through a network of radiologists (US, Indian certified) and technology-driven workflows. Daignostix, the AI arm of TRS based in Whitefield, Bengaluru, builds clinical AI for medical imaging and healthcare workflows. TRS also maintains an active academic and research focus, including AI in radiology (www.radguru.net). Role Summary: Daignostix builds AI for healthcare workflows where scanned pages, PDFs, forms and image-based text must be turned into reliable, structured data that downstream systems can trust at production scale. As a Senior AI Engineer - OCR / VLM, you will own OCR, VLM and Document AI systems end to end, from preprocessing and recognition through structured extraction, evaluation, deployment and monitoring, choosing between OCR-first, VLM-first and hybrid architectures based on evidence. Healthcare-document experience is welcome but not required; shipping substantial OCR/VLM systems is. Roles and Responsibilities: ● Architecture and ownership. Own OCR/Document AI system design: model-versus-rule trade-offs, interfaces, confidence and fallback strategy, performance targets and rollout criteria. ● OCR and VLM models. Train, fine-tune and benchmark text detection, recognition and VLM approaches. Choose OCR-first, VLM-first or hybrid pipelines on extraction quality, traceability, hallucination risk, latency and cost. ● LLM/VLM fine-tuning and small models. Fine-tune and adapt small language and vision-language models (SFT, LoRA/QLoRA, distillation from larger models) for extraction and domain tasks, and benchmark them against larger and API models on quality, latency, cost and on-prem deployability. ● Document understanding. Build pipelines that recover structure (reading order, tables, key-value pairs, fields) with confidence scores, provenance and human-review paths. ● Preprocessing. Build robust preprocessing for scanned and camera-captured inputs, including orientation, deskewing, dewarping, denoising and multi-page PDF/TIFF handling, without degrading source quality. ● Multimodal RAG and agentic systems. Build multimodal RAG over text, document images and scans with grounded, cited answers, and tool-using agents with skills and tool interfaces (function calling, MCP) and human-in-the-loop checks. ● Medical data. Work with DICOM CT and X-ray studies, non-DICOM scans and handwritten technologist sheets, with PHI-safe handling. ● Evaluation and data. Define release criteria (CER/WER, field-level and end-to-end accuracy). Own annotation guidelines, golden test sets and error taxonomies, and prevent leakage and duplicate-document contamination. ● Harness engineering. Build evaluation and agent harnesses (golden sets, replayable traces, versioned prompts and tools, CI regression gates) so model, prompt or tool changes cannot silently degrade behaviour. ● Production engineering. Package pipelines as production services (Python, Docker, FastAPI) with CPU/GPU inference, batching, versioning, logging and profiling. Optimise with ONNX/TensorRT where justified. ● Testing and technical leadership. Own unit, regression and end-to-end tests and CI/CD checks so changes cannot silently alter outputs. Review designs and code, and mentor less-experienced engineers. Requirements: Must-have: ● Education. B.Tech/B.E./M.Tech/MS/M.Sc. from an IIT, IISc, IIIT, BITS Pilani or a comparably selective institution, in Computer Science, Electrical/Electronics Engineering, AI, Data Science, Mathematics, Statistics or a related quantitative field, with a strong academic record. ● Experience. 5-8 years of hands-on experience in OCR, VLMs/multimodal AI, Document AI or applied computer vision, with ownership of real production systems beyond prototypes. ● Proof of building. Personally drove substantial OCR, VLM or Document AI systems into production, and can defend data, architecture, evaluation and deployment trade-offs in depth. ● Programming. Production-grade Python, deep PyTorch or TensorFlow/Keras, and strong NumPy and OpenCV. ● OCR and VLM depth. Text detection and recognition, transformer OCR, layout analysis and table/field extraction, plus VLM schema-constrained extraction, grounding and hallucination/omission evaluation. ● LLM/VLM fine-tuning. Hands-on fine-tuning of small LLMs/VLMs (SFT, LoRA/QLoRA/PEFT), including dataset curation, evaluation against baselines and serving the tuned models. ● Multimodal RAG and agents. Hands-on RAG (embeddings, vector search, hybrid retrieval, reranking, grounding and citations) and tool-using LLM/VLM agents (tool calling, skills/MCP, orchestration, guardrails). ● Harness engineering. Evaluation and test harnesses for LLM/VLM/agent systems: golden sets, replayable traces, prompt/tool versioning, CI regression gates. ● Production engineering. Linux, Git, Docker, FastAPI, pytest, CI/CD, and structured logging and monitoring. Good-to-have: ● Medical imaging. DICOM CT/X-ray handling (pydicom, pynetdicom, SimpleITK), non-DICOM scan ingestion and handwritten technologist-sheet recognition. ● OCR/VLM tooling. PaddleOCR, Tesseract, TrOCR, PARSeq, DBNet/CRAFT, LayoutLM/Donut or Hugging Face multimodal stacks. ● Efficient inference. LoRA/PEFT, quantisation and efficient multimodal inference. ● Domain. Experience with healthcare documents and clinical vocabulary. Key Skills: Python | PyTorch | TensorFlow / Keras | OpenCV | OCR / Text Detection & Recognition | Transformer OCR | Vision-Language Models (VLMs) | Hybrid OCR+VLM Pipelines | Structured Extraction | Layout / Table / Key-Value Extraction | Hugging Face | CER / WER | Hallucination / Omission Analysis | Confidence Calibration | Docker | FastAPI | pytest | CI/CD | ONNX / TensorRT | MLflow / Weights & Biases | Multimodal RAG | Vector Databases | Agentic AI / LLM Agents | Tool Calling / MCP | LangChain | LangGraph | Context Engineering | Prompt Engineering | Harness Engineering | LLM Evaluation (Ragas / DeepEval) | LLM Observability (Langfuse) | DICOM / Medical Imaging | Handwritten Text Recognition | LLM / VLM Fine-tuning | Small Language Models (SLMs) | LoRA / QLoRA / PEFT Why Join Daignostix: ● Real healthcare document intelligence. Build systems that work on imperfect real-world documents and produce traceable outputs downstream workflows can trust. ● End-to-end scope. Work across ingestion, preprocessing, OCR, VLM-based understanding, APIs, deployment and monitoring. ● Freedom to choose the right approach. Dedicated compute to benchmark OCR-first, VLM-first, hybrid, open-source and custom approaches on measurable quality, latency and cost.
