Senior Developer — Agentic Clinical Workflow & Orchestration

SAIGroup; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Benefits offered by this job

Competitive compensation

High-compute resources

Freedom to publish and innovate

Job summary

A cutting-edge technology firm is seeking a Senior Developer to design workflow-centric AI agents for clinical applications. This role focuses on orchestrating multi-agent systems that enhance efficiency in healthcare contexts, involving integration with imaging, clinical records, and operational systems. Candidates should possess strong experience in LLM agents, workflow automation, and a solid understanding of hospital workflows, particularly in imaging-heavy specialties. Competitive compensation and collaborative environment included.

Qualifications

• Strong experience in LLM agents, workflow automation, or multi-agent systems.
• Background in multimodal or clinical AI is a plus.
• Familiarity with hospital workflows, especially in imaging-heavy specialties, is strongly preferred.

Responsibilities

• Design and implement workflow-centric AI agents.
• Architect AI agents that can execute multi-step clinical workflows.
• Design orchestration frameworks for clinical tasks.
• Develop workflow-aware controllers and state machines for long-running processes.
• Collaborate with clinicians to ensure safe, effective AI-assisted workflows.

Skills

Experience in LLM agents

Workflow automation

Multi-agent systems

Clinical AI knowledge

Familiarity with hospital workflows

Designing agent orchestration frameworks

Collaboration with clinicians

Clinical AI

Tools

PyTorch

JAX

Transformers

AutoGen

CrewAI

LangChain

Job description

Senior Developer — Agentic Clinical Workflow & Orchestration

About the Role

We are developing a next-generation multimodal foundation model capable of understandingmedical imaging,clinical documents, andoperational context.
We are now buildingagentic systems that can execute, coordinate, and optimizeclinical workflows across radiology, oncology, and cardiology.

As a Senior Research Scientist, you will design and implementworkflow-centric AI agents, includingmulti-agent orchestration,task routing, andclinical workflow automation.
Your work will directly shape how AI collaborates with clinicians and hospital operations teams.

What You Will Work On

1. Development of Agentic Clinical Workflows (Core Focus)

• Architect AI agents that can execute multi-step clinical workflows end to end, such as:
  • Imaging ordering → prioritization → acquisition → reporting → follow-up
  • Oncology staging → treatment planning → monitoring → documentation
  • Cardiology testing → diagnosis → risk stratification → longitudinal care
• Encode clinical logic, workflow dependencies, safety steps, and escalation patterns into agent behaviors.
• Build agent pipelines that coordinate across radiology, oncology, cardiology, ED, OPD, and inpatient teams.

2. Orchestration of Multi-Agent Systems

• Design orchestration frameworks that break down complex clinical tasks intoatomic sub-tasks and route them to specialized agents (e.g., imaging analysis agent, summarization agent, scheduling agent, retrieval agent).
• Build controllers that enable agents to share context, verify each other’s outputs, and collaborate dynamically.
• Implement workflow-aware state machines or controllers to manage long-running, multi-step clinical processes.

3. Integration With Imaging, Clinical, and Operational Systems

• Develop agents that interact with:
  • Imaging systems (PACS/RIS)
  • Clinical records (EHR/HIS)
  • Operational systems (scheduling, triage, queue management)
• Ensure orchestration layers understand real-world constraints like department availability, urgency, and imaging backlogs.
• Build agents capable of responding to workflow events (urgent findings, incomplete scans, patient no-shows, etc.).

4. Leveraging the Multimodal Foundation Model

• Use the foundation model’s imaging-text representations to build agents that can:
  • Understand 3D studies (CT/MRI/PET)
  • Read clinical notes and operational metadata
  • Perform reasoning across multimodal patient context
• Apply prompting, tool use, retrieval augmentation, and memory mechanisms to power agent workflows.

5. Evaluation, Safety & Reliability

• Define evaluation methods for agentic workflows, including:
  • Workflow completion accuracy
  • Task routing correctness
  • Operational efficiency improvements
  • Clinical coherence and safety
• Collaborate with clinicians to identify edge cases and ensure agents support safe clinical practice.
• Build monitoring and guardrail mechanisms for workflow-level safety.

Why This Role Is High-Impact

• You are designing theworkflow brain of next-generation clinical AI systems.
• Your work shapes AI’s role in radiology, oncology, and cardiology operational environments.
• You define new architectures for orchestrated AI that improve clinical efficiency and patient care.
• You help build the world’s firstagentic clinical coordination layer on top of a multimodal foundation model.

What We’re Looking For

• Strong experience in LLM agents, workflow automation, or multi-agent systems.
• Background in multimodal or clinical AI is a plus.
• Familiarity with hospital workflows—especially in imaging-heavy specialties—is strongly preferred.
• Ability to design and implement agent orchestration frameworks.
• Hands‑on experience with PyTorch/JAX, transformers, retrieval systems, or agent frameworks (AutoGen, CrewAI, LangChain, etc.).
• Ability to collaborate with clinicians and product teams to translate real-world workflow needs into agentic systems.

Nice to Have

• Experience with PACS/RIS/EHR integration, clinical informatics, or operational workflow tools.
• Knowledge of oncology and cardiology care pathways.
• Prior work with workflow engines, BPMN systems, or orchestration controllers.
• Experience running simulations, RL-based workflow optimizers, or task planners.

What We Offer

• Competitive compensation commensurate with productivity and innovation.
• Opportunity to define the orchestration layer for a new generation of clinical AI.
• High-compute resources.
• Close collaboration with clinicians, operations leaders, and AI researchers.
• Freedom to publish and innovate in an emerging field.