AI Research & Development Lead

We are seeking an AI R&D Lead to lead a multidisciplinary team building LLM and Multimodal AI systems for clinical use within Intelehealth’s telemedicine platform. This is a techno-functional leadership role requiring deep expertise in modern AI systems alongside strong grounding in clinical safety, regulatory frameworks, and Software as a Medical Device (SaMD). The role combines hands-on technical contribution with team leadership, ensuring that AI systems are not only effective, but clinically safe, auditable, and deployable in real-world health systems. This role is for an intrapreneurial interdisciplinary thinker who is excited at innovating and translating advances in generative AI to products that benefit vulnerable communities. Key Responsibilities 1. Technical and Research Leadership Define and execute the roadmap for LLM and Multimodal AI across the organization Make architectural decisions across prompting, fine-tuning, retrieval (RAG), and Multimodal system design Set standards for model evaluation, safety, and deployment 2. Team Leadership and Management Build, manage, and mentor a team of AI/ML researchers, engineers, clinicians Establish clear goals, execution plans, and performance expectations for the team Create a culture of rigor, documentation, and accountability—especially around clinical risk Balance research exploration with product delivery timelines 3. Clinical Safety and Risk Oversight Own safety frameworks for AI systems, including hallucination mitigation and fail-safe design Ensure human-in-the-loop systems are appropriately designed and implemented Oversee structured risk assessments and alignment with clinical protocols 4. Regulatory and SaMD Alignment Drive alignment of AI systems with SaMD principles and regulatory expectations Ensure documentation, traceability, and audit ability across the model lifecycle Work with leadership and partners on regulatory strategy and compliance pathways 5. Multimodal and LLM System Development Guide development of systems combining text, voice, image, and structured data Oversee adaptation of foundation models for low-resource, high-variability environments Ensure systems are robust to real-world data quality issues 6. Cross-functional Execution Partner with product, engineering, and clinical teams to translate research into deployable features Act as the primary interface between AI, clinical stakeholders, and external partners Support fundraising and strategic partnerships with clear articulation of AI capabilities and safeguards 7. Innovation at the last mile Design and deploy LLM and Multimodal AI systems for low-resource settings, accounting for constraints such as limited connectivity, low-spec devices, and variable data quality Ensure robustness to missing or noisy inputs and optimize for offline-first or low-bandwidth environments Adapt systems for usability by frontline health workers with varying levels of clinical training and digital literacy Incorporate contextual factors (e.g., health system fragmentation, population diversity, and infrastructure variability) into model design, evaluation, and deployment Requirements Qualifications & Experience Advance degree (PhD, MD, or equivalent experience) in machine learning, biomedical engineering, computer science, or related field. At least 6 years of experience overall, including at least 1 year of significant work with LLMs and/or Multimodal systems. Prior experience leading and managing technical teams Demonstrated experience deploying AI systems in healthcare Demonstrated experience building or deploying AI/ML or digital health systems in low-resource or high-variability environments, with an understanding of constraints such as limited infrastructure, data quality challenges, and end-user usability in frontline care settings Experience in healthcare, digital health, or global health settings Key Competencies Strong hands-on expertise in: a. Large language models (prompting, fine-tuning, evaluation, guardrails) b. Multimodal systems (vision-language models, speech interfaces) c. Knowledge grounding approaches (RAG, structured clinical knowledge systems) Direct experience with Software as a Medical Device (SaMD) development and lifecycle Familiarity with regulatory frameworks (e.g., FDA, CE, CDSCO, or equivalent) Benefits Remote working. Flexible working hours. Great work culture. 5 days working. Group Medical Insurance.