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 protected, 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 reasoning
and
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.

📌 Senior Agentic Ai Engineer Agentic Medical Ai (Bengaluru)
🏢 SAIGroup
📍 Bengaluru