AI Engineer

Job Description

-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 (www.radguru.net) 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-functio

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