You're not {everyone} You have no off switch. You're obsessed with revolutionizing healthcare. Your default mode is building what's never been built before. You see the gaps in healthcare technology and you're driven to bridge them. Every problem is an opportunity, and you won't rest until you've created something extraordinary. Forget the traditional application. You don't need a perfectly formatted CV to show that you're exceptional — your work speaks for itself. You've already proven your ability to innovate and execute. Now you're ready to transform the future of healthcare technology. Requirements

• Design and implement end-to-end AI/ML solutions from data prep to production deployment. • Build RAG pipelines with robust retrieval, re-ranking, grounding, and evaluation. • Develop AI applications such as conversational agents and document intelligence workflows (extraction, classification, validation). • Implement image/document processing (OCR, layout analysis, table extraction, redaction). • Integrate with enterprise systems and APIs; enable tool use/function calling where applicable. • Optimize for latency, accuracy, cost, and safety. • Package and deploy with Docker/Kubernetes/Serverless; automate with CI/CD. • Write clean, testable code; contribute to internal accelerators, templates, and best practices.

• Developing and applying models and algorithms. • Training, validating, and optimizing Machine Learning and Deep Learning models. • Integrating Generative AI and LLMs into various solutions. • Documentation, versioning, and maintenance of models. • Developing, maintaining, and optimizing high-quality, reliable, and robust data pipelines. • Extraction, cleaning, and validation of large datasets. • Data interpretation to uncover solutions and business opportunities. • Data analytics using Business Intelligence tools such as Apache Superset.

• Lead the design and implementation of Generative AI services and Agentic workflows that support multiple product features or teams. • Integrate LLMs into applications using modern frameworks, working with APIs or internal model endpoints. Implement telemetry, observability, fallbacks, and cost/latency controls. • Work across data environments to ingest, transform, and serve data for AI use cases, designing practical schemas and retrieval strategies that generalize across environments. • Design and run experiments to compare prompts, models, and configurations; build evaluation flows to measure relevance, safety, robustness, and business impact. • Collaborate with product, data science, design, and domain experts to clarify requirements, break down initiatives into technical plans, and deliver roadmap commitments. • Contribute to and lead code reviews, architecture discussions, documentation, and shared templates/libraries that improve velocity and consistency. • Monitor AI systems in production, participate in incident response, and drive systemic improvements to quality, safety, reliability, and performance. • Partner with security, legal, and compliance to ensure data privacy, responsible AI practices, and regulatory alignment.