Why This Role Appeals to A rare chance 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 current 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.
Solid 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.

📌 Agentic Ai Research Bengaluru (India)
🏢 Saiharmony Solutions India
📍 India