AI Engineer

About Company:

Teleradiology Solutions (TRS) is a pioneer in teleradiology, delivering round-the-clock diagnostic radiology reporting and support to hospitals and healthcare providers across India, the US, and other global markets. Founded in 2002 by two Yale-trained physicians and headquartered in Ardmore, PA, TRS today serves over 150 hospitals across 21 countries.
The company pairs a network of board-certified radiologists — including ABR-certified specialists — with technology-driven workflows and its AI-enabled arm, dAIgnostiX, to deliver fast, accurate, and reliable imaging interpretation. dAIgnostiX is based out of Bengaluru (Whitefield).  TRS/dAIX also maintains an active academic focus and ongoing research interests, including AI in radiology.

Experience: 2+ Years
Location: Bengaluru / On-site
Department: Artificial Intelligence

About the Role

We are looking for a hands-on AI Engineer with 2+ years of experience to design, build, and deploy production-grade AI systems, with strong focus areas in Generative AI / LLMs, OCR-based document/data extraction, and Computer Vision (classification, object detection, segmentation). The ideal candidate is comfortable working across the full stack — from model development/integration to backend APIs to deployment.

Key Responsibilities

• Design, develop, and deploy GenAI/LLM-based solutions (RAG pipelines, prompt engineering, fine-tuning, agentic workflows).

• Build and optimize OCR pipelines for extracting structured data from scanned documents, PDFs, images, and forms.

• Develop, train, and fine-tune Computer Vision models for image classification, object detection, and segmentation tasks.

• Develop and maintain RESTful APIs using FastAPI to expose AI/ML models and services.

• Containerize applications using Docker and manage deployment across environments.

• Design and manage data storage using MongoDB (NoSQL) and SQL databases.

• Integrate LLM APIs (OpenAI, Anthropic Claude, open-source LLMs like LLaMA/Mistral) into production applications.

• Work with vector databases (FAISS, Pinecone, Chroma, Weaviate, etc.) for semantic search and RAG implementations.

• Collaborate with cross-functional teams (product, backend, DevOps) to integrate AI features into existing platforms.

• Write clean, maintainable, well-documented, and testable code.

• Monitor model performance, debug issues, and iterate on solutions based on production feedback.

• Stay current with advancements in GenAI, LLMs, OCR, and Computer Vision.

Required Skills & Qualifications

• 2+ years of experience in AI/ML engineering.

• Strong proficiency in Python.

• Hands-on experience with GenAI / LLMs — prompt engineering, RAG, embeddings, fine-tuning, or agentic frameworks (LangChain, LlamaIndex, LangGraph or similar).

• Practical experience with OCR technologies (Tesseract, PaddleOCR, AWS Textract, Google Vision OCR, Azure Form Recognizer, or similar) for document/image data extraction.

• Hands-on experience with Computer Vision — image classification, object detection (YOLO, Faster R-CNN, etc.), and segmentation (U-Net, Mask R-CNN, semantic/instance segmentation) using frameworks like PyTorch, TensorFlow, or OpenCV.

• Solid experience building APIs with FastAPI.

• Working knowledge of Docker — building images, writing Dockerfiles, docker-compose.

• Experience with MongoDB and SQL databases (schema design, queries, indexing, aggregation).

• Understanding of core ML/DL concepts (CNNs, transformers, embeddings, evaluation metrics like IoU, mAP, F1).

• Familiarity with version control (Git) and basic CI/CD practices.

• Good understanding of REST API design principles and asynchronous programming in Python.

Good to Have

• Experience with vector databases and semantic search.

• Exposure to cloud platforms (AWS/Azure/GCP), especially their AI/ML and storage services.

• Experience with model training/fine-tuning pipelines (Hugging Face, PyTorch, Detectron2, MMDetection).

• Familiarity with medical imaging formats (DICOM) or other domain-specific imaging pipelines.

• Familiarity with message queues (RabbitMQ/Kafka) for async processing.

• Experience with monitoring/logging tools for production AI systems.

• Prior experience in healthcare, fintech, or document-heavy domains is a plus.

Soft Skills

• Strong problem-solving ability and willingness to work across the stack.

• Ability to work independently and in a fast-paced, iterative environment.

• Good communication skills to collaborate with cross-functional teams.