Senior Agentic AI Engineer Agentic Medical AI

About the Role

We are building a new class of clinical-grade AI agents powered by a foundational model that unifies 3D volumetric medical data and textual reasoning. These agents will assist with radiology workflows, treatment planning, case summarization, longitudinal patient analysis, and human-in-the-loop clinical decision support.

In this role, you will design the agent layer that transforms a powerful multimodal foundation model into a safe, reliable, adaptive medical assistant capable of analyzing 3D scans, retrieving past cases, explaining findings, and coordinating multi-step reasoning chains.

This position is ideal for researchers from multimodal/VLM/CLIP ecosystems who want to move up the stack into autonomous medical reasoning, multimodal alignment, and real-world clinical impact.

What You Will Work On

• Architect LLM-driven medical agents capable of:




• Reading & interpreting 3D medical volumes (MRI, CT, PET).


• Answering radiology questions, generating report drafts, and performing differential reasoning.


• Retrieving similar prior cases using contrastive embeddings.


• Synthesizing multi-instance evidence across scans, labs, and notes.


• Executing multi-step “clinical workflows”—image → analysis → summary → recommendation.




• Integrate clinical agents with the 3D foundation model’s representations, enabling structured medical reasoningand contextual memory across longitudinal data.


• Develop evaluation frameworks for trustworthiness, interpretability, calibration, and clinical safety.


• Collaborate with clinicians and scientists to co-design tasks, datasets, and validation protocols.


• Benchmark models against open and proprietary datasets: MIMIC, CheXpert, MosMed, LiTS, BraTS, and India-specific clinical corpora.


• Publish findings at top-tier venues (MICCAI, NeurIPS, AAAI, EMNLP).


• Work with regulatory and product teams to translate research into medically viable systems.

Why This Role Appeals to CLIP / VLM / Chitrarth / Patram Authors

• A rare opportunity to work at the intersection of agentic LLMs, multimodal 3D vision, and clinical intelligence.


• Ability to bring contrastive learning, cross-lingual modeling, multimodal alignment, and document understanding into the medical domain.


• Greenfield space: medical agent design is still unexplored compared to 2D VLMs.


• Freedom to publish, open source, and propose new clinical reasoning benchmarks.


• Attractive to researchers who want impact beyond academic settings—directly improving physician workflows and patient outcomes.


• A chance to create India’s first medical foundation-model agent system, setting global standards.

What We’re Looking For

• Experience developing LLM-based agents, tool-using models, chain-of-thought systems, or orchestrated pipelines.


• Strong background in multimodal learning, vision–language transformers, contrastive models, or clinical NLP.


• Familiarity with medical imaging, radiology workflows, DICOM pipelines, or 3D data representations.


• Expertise in PyTorch/JAX and large-scale training and inference.


• Ability to design safe, interpretable, and auditable reasoning systems.


• Strong publication record in multimodal, medical AI, agentic systems, or LLMs.


• Comfort collaborating with clinicians and biomedical researchers.

Nice to Have

• Experience with agent frameworks (LangChain, LLaMA Index, Haystack, custom pipelines).


• Background in clinical ontologies, SNOMED, RadLex, or UMLS.


• Contributions to medical AI challenges (BraTS, RSNA, MedVQA, MedIC).


• Prior work in multilingual or code-mixed medical text.


• Proven ability to take research concepts into production-grade systems.

What We Offer

• Competitive compensation.


• Access to high-compute clusters + large proprietary datasets.


• Full freedom to publish and define development direction.


• Cross-functional collaboration with clinicians, regulatory experts, and product teams.


• A clear path to building systems that meaningfully improve healthcare outcomes.