Sai Bhandari
Founding Engineer (AI Backend) at Solstice Health
About
I'm Sai Bhandari, currently the Founding Engineer (AI Backend) at Solstice Health. My journey has taken me from a background in mechanical engineering to building high-availability systems and agentic AI for the life sciences sector. At Solstice, I focus on creating AI that doesn't just answer prompts but plans and executes autonomously within the strict guardrails of FDA compliance. I'm passionate about the 'invisible' side of engineering—things like latency, throughput, and system resilience—and I believe that trust is the ultimate currency for AI adoption in regulated industries. I'm currently looking to grow our team with talented AI and full-stack engineers, and I'm always open to deep-dive conversations about scaling intelligent workflows or the intersection of AI and human values.
Networking
What I can offer
- ›Insights on building agentic systems
- ›Expertise in scaling intelligent workflows in regulated industries
- ›Guidance on SOC 2 compliance and high-availability architecture
- ›Hiring opportunities at Solstice Health
Looking for
- ›AI engineers and full-stack developers
- ›Sales leadership candidates
- ›Technical discussions on balancing fairness and latency in inference systems
Best fit for
Current Interests
Background
Career
Transitioned from mechanical engineering to software development at Infosys, followed by a Master's in IT at ASU where he focused on high-availability systems, eventually becoming a Founding Engineer in the AI health-tech space.
Education
Master of Science in Information Technology, Arizona State University (2022 – 2024); Bachelor of Engineering (Mechanical Engineering), Savitribai Phule Pune University (2016 – 2020).
Achievements
- ›Contributed to AI platforms used by over 50 biopharma brands
- ›Led/contributed to achieving SOC 2 Type 2 compliance at Solstice Health
- ›Engineered a mobile application for 5,000+ users with 40+ RESTful API endpoints
- ›Achieved 95% code coverage through unit and integration testing
Opinions
- The AI hype bubble cooling down is a positive cycle that filters out noise for real value-driven use cases.
- Trust and reliability are the primary requirements for AI adoption in regulated industries.
- Scaling AI is less about 'magic' and more about rigorous systems engineering and operating systems theory.
- AI should inspire trust and align with human values rather than just automating tasks.