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 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.
• 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.

📌 Senior Agentic AI Engineer — Agentic Medical AI (Bengaluru)
🏢 SAIGroup
📍 Bengaluru