Yash Solanki
I’m a Full Stack and AI/ML Engineer with 3+ years of experience building production-grade machine learning systems and scalable web applications end to end.
About
I’m a Full Stack and AI/ML Engineer with 3+ years of experience building production-grade machine learning systems and scalable web applications end to end. My background spans backend engineering, modern UI development, and cloud-native deployments, allowing me to deliver reliable, high-performance products rather than just prototypes.
I’ve built and shipped real-world AI and web applications, with a strong focus on integrating ML models into robust backend systems and user-facing products. My work includes optimizing AI pipelines for latency and scalability, designing clean APIs, and deploying solutions on cloud infrastructure.
I also collaborated with Meta on a cutting-edge multi-modal XR medical tutoring system, where I worked on AI-driven immersive learning experiences. This experience strengthened my ability to work on complex, research-driven projects while delivering practical, user-impactful outcomes.
I’m passionate about data-driven product development, enjoy solving hard technical problems, and thrive at the intersection of machine learning, software engineering, and real-world usability.
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- 3 years' experience
- Joined August 2025
Industry experience
- Education
- Healthcare
- Software & Internet
- Professional Services
- Media & Entertainment
Work experience· 4 roles
Led the development of enterprise-grade GenAI applications by writing robust, modular Python code for production environments, architecting RAG pipelines with LangChain and vector databases to extract insights from unstructured data. Deployed models using Docker and FastAPI to ensure scalable, seamless integration with business applications, and collaborated closely with data engineers to optimize data ingestion workflows, reducing model context retrieval latency by 35%.
Developed an AI-powered XR tutoring platform using Meta Ray-Ban smart glasses for immersive medical education. Trained a custom YOLOv8 model on over 5,000 images for real-time anatomical detection. Fine-tuned LLaMA 3 with reinforcement learning from human feedback (RLHF) to improve interactive learning quality. Optimized multi-modal AI pipelines to achieve low-latency performance.
Built more than 10 end-to-end applications combining machine learning models with robust backend systems. Delivered AI-driven solutions for various use cases including classification, recommendations, and automation. Deployed projects using FastAPI, Docker, AWS with fully automated CI/CD pipelines.
Education
Certifications and awards
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