Hi, I'm Sreenidhi, a software/electrical engineer who also builds community programs.
I find where systems break and work with people to build fixes that stick.
8+ years across embedded IoT firmware, engineering-team enablement, community programs, and AI tool testing: same instinct applied to firmware bugs, unclear requirements, and unreliable model outputs.
Available now for technical program management, technical product, and AI enablement roles.
- Ivani: firmware debugging, data pipeline, and ML algorithm work on a 1,000+ node IoT deployment.
- Embark Women: co-founded a business community in Tyler, TX. Coordinated about 10–15 volunteers and helped raise around $5,000.
- AI adoption: tested prompting on real engineering queries, and the guidance spread across about 10 engineers in about two weeks.
- Hackathon-judged prototypes, from a Slack onboarding agent to a code-search knowledge graph.
AI adoption at Ivani: reusable prompt templates, a cheat sheet, and context guidance that spread from two engineers to roughly ten in about two weeks.
What does this mean?
A self-run comparison of original vs. structured prompts on real engineering queries at Ivani. The savings estimates it produced are directional observations, not controlled measurement; see the case study for those figures and their limits.