Clinical Validation Lead — AI & Medical Imaging
Location: ClayWorks Shankaraa, Doddakallasandra, Bengaluru
Company: Drongo AI
About Us
Drongo AI develops intelligent enterprise software across healthcare, medical imaging and AI. Our backend systems power scalable applications handling medical imaging, AI inference, enterprise workflows, and cloud-native services.
Job Summary
We are seeking a Clinical Validation Lead to establish and lead the clinical evidence strategy for our AI-enabled medical-imaging products. You will ensure our solutions are evaluated rigorously, reflect real-world clinical practice, and generate credible evidence for regulatory submissions, product development, and clinical adoption.
This is a cross-functional leadership role at the intersection of clinical medicine, imaging, artificial intelligence, biostatistics, and regulatory science. You will work closely with clinicians, research scientists, engineers, product leaders, regulatory specialists, and external research partners.
Key ResponsibilitiesClinical validation strategy
- Develop the clinical validation and evidence-generation strategy across the product lifecycle.
- Translate intended use, clinical claims, and product requirements into robust validation plans and acceptance criteria.
- Define appropriate study designs, endpoints, patient populations, comparators, and performance thresholds.
- Ensure studies evaluate clinical utility as well as technical model performance.
- Identify evidence gaps and recommend studies that support regulatory clearance, product development, market access, and clinical adoption.
Study design and execution
- Lead retrospective and prospective validation studies, including multi-site studies and reader-performance studies.
- Develop study protocols, statistical analysis plans, case report forms, image-review workflows, and clinical data specifications.
- Establish ground-truth and adjudication processes using qualified clinical experts.
- Select evaluation methods and metrics appropriate to the intended use, such as sensitivity, specificity, ROC-AUC, predictive values, calibration, agreement, workflow impact, and time savings.
- Evaluate model performance across sites, imaging devices, acquisition protocols, patient subgroups, disease prevalence, and clinically relevant edge cases.
- Coordinate with hospitals, imaging centres, principal investigators, contract research organisations, and other external partners.
- Oversee study timelines, risks, budgets, documentation, and deliverables.
Clinical and regulatory evidence
- Ensure clinical evidence is scientifically rigorous, reproducible, traceable, and aligned with applicable regulatory and quality requirements.
- Contribute clinical-validation content to regulatory submissions, technical documentation, risk-management files, and responses to regulatory questions.
- Partner with regulatory and quality teams on Good Clinical Practice, data integrity, privacy, ethics approvals, and post-market evidence requirements.
- Support the development of scientific publications, conference abstracts, clinical presentations, and peer-reviewed manuscripts.
- Communicate study findings clearly, including limitations, residual risks, and implications for product claims.
Cross-functional leadership
- Serve as the clinical-validation subject-matter expert for medical-imaging AI.
- Work with product and clinical teams to define intended users, clinical workflows, intended-use populations, and meaningful product claims.
- Partner with machine-learning and data teams on dataset suitability, annotation quality, bias analysis, failure-mode analysis, and model generalisability.
- Translate validation findings into actionable recommendations for product and model improvement.
- Build scalable validation processes, templates, governance, and review standards.
- Present evidence and recommendations to senior leadership and external clinical stakeholders.
Required qualifications
- Advanced degree in medicine, medical imaging, clinical research, biomedical engineering, biostatistics, epidemiology, or a related field.
- Significant experience designing and leading clinical-validation studies for medical devices, diagnostics, digital health products, or medical-imaging technologies.
- Strong understanding of diagnostic-accuracy studies, clinical endpoints, sources of bias, sample-size considerations, and statistical interpretation.
- Experience working with medical-image data and imaging workflows, such as radiology, pathology, ophthalmology, cardiology, or another image-intensive specialty.
- Familiarity with AI or machine-learning model evaluation, including generalisability, dataset shift, subgroup performance, calibration, and clinically meaningful error analysis.
- Experience collaborating with clinical investigators, research sites, data scientists, product teams, and regulatory or quality functions.
- Excellent scientific writing, critical-thinking, project-management, and stakeholder-communication skills.
- Ability to balance scientific rigour with the pace and practical constraints of product development.
Preferred qualifications
- Clinical qualification or experience practising in a relevant imaging specialty.
- Experience with reader studies, multi-reader multi-case analysis, prospective studies, or real-world performance monitoring.
- Familiarity with DICOM, PACS, radiology information systems, imaging protocols, and clinical workflow integration.
- Experience supporting regulatory submissions for software as a medical device or AI-enabled medical devices.
- Knowledge of FDA, EU MDR, UK, or other international medical-device evidence requirements.
- Experience with annotation programmes, clinical adjudication panels, or imaging core laboratories.
- Track record of peer-reviewed publications or conference presentations.
- Experience leading teams, vendors, or external research collaborations.
Pay: From ₹700,000.00 per year
Benefits:
- Provident Fund
- Work from home
Work Location: In person
