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 the AI behind TRS's 24x7 teleradiology operations and the products used by radiologists, hospital partners and internal teams. Beyond clinical imaging models, this includes algorithms that route and prioritise cases, predict TAT and forecast volumes and radiologist coverage, along with mobile applications that put these capabilities in users' hands. As a Junior AI Engineer – Applied AI (Non-Clinical), you will build and ship operational ML algorithms and AI-enabled mobile features end to end under the guidance of senior engineers, using AI-assisted development tools such as Claude Code to move quickly without compromising quality. This role does not work on clinical diagnostic models. Roles and Responsibilities ● Operational algorithms. Build, train and evaluate models for case routing and worklist prioritisation, TAT prediction, radiologist scheduling and RVU/volume forecasting, and anomaly detection on operational data. ● Mobile AI features. Build AI-enabled features in mobile apps (React Native, Flutter or native iOS/Android) and integrate them with backend model services. ● On-device inference. Convert, optimize, and deploy machine learning models such as LightGBM, XGBoost, and scikit-learn models for production use, balancing accuracy, inference latency,



memory usage, and model size. ● Data and evaluation. Build data pipelines from operational systems, define success metrics with operations stakeholders, and measure model impact against baselines in production. ● AI-assisted development. Use Claude Code and similar agentic coding tools in day-to-day development, while owning the review, testing and correctness of everything you ship. ● Production engineering. Package models as tested services (Python, Docker, FastAPI) with unit and regression tests, logging and monitoring. Senior engineers lead architecture and deployment. ● Stakeholder collaboration. Work with operations, product and engineering teams to turn workflow problems into measurable AI solutions. 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. 2-4 years of hands-on software and ML engineering, including mobile applications and ML models that reached real users. ● Proof of building. At least one mobile app or ML system that you personally built and shipped to real users, and can defend in technical depth. ● Programming. Strong Python skills with hands-on experience in machine learning frameworks and libraries such as scikit-learn, XGBoost,



and LightGBM, along with proficiency in building and deploying production-grade ML solutions. ● AI-assisted development. Regular use of Claude Code or similar agentic coding tools on real projects, with sound judgement in reviewing and testing AI-generated code. ● ML fundamentals. Tabular, time-series and optimisation problems: feature engineering, gradient-boosted models, forecasting, classification metrics and experimental design. ● Engineering basics. Linux, Git, Docker, REST APIs, pytest and basic CI/CD. Good-to-have: ● On-device ML. Experience deploying and optimizing machine learning models such as LightGBM, XGBoost, and scikit-learn models, including model compression, inference optimization, and efficient production deployment. ● Operations research. Scheduling, queueing or routing optimisation (e.g., OR-Tools). ● LLMs and cloud. LLM APIs, RAG or agent workflows, and one of AWS, Azure or GCP. ● Shipping experience. App store releases, mobile analytics and exposure to healthcare workflows. Key Skills Python | scikit-learn | XGBoost / LightGBM | Time-Series Forecasting | Optimisation / OR-Tools | React Native / Flutter | Swift / Kotlin | PyTorch | Core ML / TensorFlow Lite / ExecuTorch | Claude Code / AI-Assisted Development | FastAPI / REST APIs | Docker | Git | pytest | CI/CD | MLflow / Weights & Biases Why Join Daignostix  ● AI that runs the operation. Your algorithms directly shape how cases flow across a 24x7 global teleradiology service. ● Ship to real users. Build mobile and backend features used by radiologists, hospital partners and internal teams. ● Contemporary tooling with mentorship. Work with Claude Code and current AI tooling, with senior technical review and visibility from build to production.

📌 Junior AI Engineer – Applied AI (Bengaluru)
🏢 Teleradiology Solutions
📍 Bengaluru