Senior AI Engineer

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 clinical AI for medical imaging, with algorithms spanning neuro and stroke, pulmonary embolism, aortic pathology, urology, spine and rib fracture, solid-organ trauma and paediatric chest. Our models run on real patient studies alongside radiologists (US, Indian certified), so success means clinical accuracy, robustness, latency and safe operation in production.

As a Senior AI Engineer, you will own clinical AI algorithms end to end: problem formulation, data and ground truth, modelling, clinical evaluation, production deployment and monitoring. This is a hands-on senior individual-contributor role for an engineer who has already shipped and operated production AI systems.

Roles and Responsibilities:

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  End-to-end algorithm ownership. Own one or more clinical AI algorithms from proposal through data strategy, modelling, validation, deployment, monitoring and improvement. Define the technical plan, flag risks early and drive decisions to closure.
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  Model development. Design and train CNN, transformer and hybrid models for classification, detection and segmentation, using classical ML where it is the better fit.
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  Data and ground truth. Architect DICOM pipelines and dataset curation with lineage and versioning. Catch leakage, label-quality issues, cohort bias and scanner/site variability before they become model problems.
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  Clinical evaluation. Define evaluation protocols, clinical metrics, calibration and subgroup analyses. Lead false-positive/false-negative review and threshold selection with radiologists (US, Indian certified).
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  Production engineering. Turn validated models into production services (Docker, FastAPI/REST) with robust GPU inference, observability and safe failure behaviour. Optimise with ONNX, TensorRT or CUDA where it measurably helps without changing clinical outputs.
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  MLOps and reliability. Deploy on AWS, Azure or GCP and on on-premise GPU infrastructure. Own experiment tracking, model registries, CI/CD, regression and golden-set testing, drift monitoring and rollback.
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  Technical leadership. Lead design, code and model-readiness reviews, mentor junior engineers and set engineering standards. This role has no people-management responsibility.

Requirements:

Must-have:

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  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.
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  Experience. 5-8 years of hands-on AI/ML engineering, including multiple years taking deep-learning systems into production. Medical-imaging or healthcare AI experience is strongly preferred.
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  Product pedigree. Substantive AI/ML engineering experience at a leading medical-imaging AI company or a top-tier AI product or research organisation, with clear ownership of production AI products.
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  Proof of production ownership. Able to walk through at least two AI/ML systems you personally drove from data through deployment, monitoring and iteration, with measurable outcomes.
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  Programming. Expert Python, deep PyTorch and/or TensorFlow/Keras, and working proficiency in NumPy, scikit-learn and OpenCV.
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  ML depth. Strong command of deep learning, computer vision, classical ML, statistics, calibration and experimental design.
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  Production engineering. Linux, Git, Docker, FastAPI/REST, pytest, CI/CD, logging and monitoring, model/data versioning, and hands-on AWS, Azure or GCP.

Good-to-have:

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  Scale and performance. Kubernetes, distributed training/inference, ONNX/TensorRT, CUDA profiling and infrastructure-as-code.
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  Medical imaging tooling. DICOM/NIfTI, MONAI and nnU-Net.
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  Multimodal AI. VLMs, LLMs, RAG and PEFT, as an addition to, not a substitute for, core deep-learning depth.

Key Skills:

Python | PyTorch | TensorFlow / Keras | NumPy | scikit-learn | OpenCV | Classical ML | CNNs | Vision Transformers | Detection | Segmentation | DICOM / NIfTI | MONAI / nnU-Net | Clinical Metrics | Calibration | Docker | FastAPI | pytest | CI/CD | MLflow / Weights & Biases | AWS / Azure / GCP | Kubernetes | ONNX / TensorRT

Why Join Daignostix:

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  Clinical AI that matters. Algorithms evaluated on real patient studies, where robustness, latency and reliability have direct operational consequences.
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  Direct access to radiologists. Work with subspecialist radiologists (US, Indian certified) on annotation strategy, threshold decisions and clinical failure analysis.
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  Senior influence, hands-on scope. Shape architecture and technical standards across GPU infrastructure, cloud and on-premise deployment, while staying close to code and data.