Hey! I'm Srinidhi, currently doing my Master's in Applied Data Intelligence at San José State University. I'm one of those people who gets genuinely excited about making things work better with code and data. Whether it's building software that scales, wrangling messy datasets into something useful, or training ML models that don't just look good on paper but actually solve problems, I'm in.

Lately, I've been kind of obsessed with Agentic AI workflows. You know, those systems where AI agents actually collaborate and get stuff done autonomously? I'm actively working on building and orchestrating these workflows, trying to push what's possible when you let intelligent agents work together. It's wild seeing them reason through problems and execute tasks that would normally need constant human oversight.

What I'm looking for: Summer 2026 internships where I can actually build cool stuff:

• Software Engineering | I want to write code that thousands (or millions) of people use
• Data Science | Turning messy data into clear insights is my jam
• Machine Learning Engineering | Building ML systems that work in production, not just notebooks
• AI Engineering | Especially anything involving agentic systems or next-gen AI applications

📍 Location: San Jose/SF Bay Area, CA | 📧 Email: [email protected] | 📱 Phone: +1 (669) 243-7083

Here's some work I'm actually proud of. Check out the repos if anything catches your eye (and maybe drop a ⭐ if you vibe with it):

Built and deployed an XGBoost churn model on AWS SageMaker that hit 84% precision. This actually helped cut customer attrition by 12% and bumped renewals by 15%. Seeing it work in production was rewarding.

Created serverless ETL pipelines with Lambda, Glue, and Airflow processing 25k+ records every single day. Improved data quality by 30%. Turns out clean pipelines equal clean data (who knew? 😄).

Spent time optimizing our Redshift warehouse by playing around with schemas and partitions until we got queries running 50% faster and slashed storage costs by 30%.

My first real taste of production data work. Built various ML models (both supervised and unsupervised) that beat our baseline by 18%. Learned a ton about what actually works vs. what just looks good in theory.

Engineered pipelines for multi-million-row datasets and got preprocessing time down by 35%. Turns out optimization matters when you're dealing with that much data. Also did comprehensive EDA which improved our feature engineering by 20%. Good old exploratory analysis never goes out of style.

This is what I work with day-to-day (and keep adding to). Always learning something new!

What drives me is pure curiosity, honestly. I can't help but tinker with new frameworks, read research papers at 2 AM (yes, really), and constantly ask "what if we did it differently?" I genuinely read research papers for fun. Yeah, I know how that sounds. But there's something satisfying about understanding how things work at a fundamental level.

I love the full spectrum, from writing clean production code to experimenting with bleeding-edge AI concepts. Here's a hill I'll die on: good data engineering is like 80% of what makes ML projects succeed. Clean pipelines beat fancy algorithms every time.

Currently exploring: Agentic AI workflows and multi-agent orchestration. Actively building systems where AI agents collaborate, reason, and execute complex tasks. It's fascinating watching them reason through complex problems. Like a really smart team that never sleeps. I really believe we're at the start of something huge with AI. Want to be someone who builds it, not just talks about it.

Also diving into advanced RAG architectures (going way beyond basic retrieval with hybrid search and re-ranking), real-time data streaming with Kafka and Flink, privacy-preserving ML techniques (because training models on sensitive data shouldn't mean exposing that data), system design at scale (learning how to build distributed systems that don't fall apart when things get big), and LLM fine-tuning to make AI models actually useful for specific tasks.

M.S. in Applied Data Intelligence | San José State University | Jan 2025 - May 2027

Taking/took classes in: Data Warehousing & Pipelines, Big Data Tech, Machine Learning, Deep Learning, Statistical Analysis, Generative AI Applications, Data Mining, Data Viz

B.E. in Electrical & Electronics Engineering | PES College of Engineering, India

Kind of obsessed with Agentic AI right now. Building systems where AI agents work together autonomously feels like we're living in the future. If I have to do something manually more than twice, I'm writing a script for it. Or lately, building an agent to do it.

Always have 3-4 side experiments running. Currently it's multi-agent systems and advanced RAG architectures. This week I'm exploring prompt engineering techniques. Last week was Kafka optimization. Learn something new every day philosophy.

Guilty pleasure: spending way too long making the perfect data visualization. Color schemes matter, okay? Run on coffee and curiosity. Mostly curiosity about why things work the way they do. I like both sides of the coin. Building rock-solid production code AND experimenting with wild, experimental ideas. Why choose?

Still figuring out privacy-preserving ML but it's important work. Reading everything I can get my hands on. Research papers, tech blogs, docs, you name it. If it's about AI, data, or software engineering, I'm probably reading it.

Seriously, I love talking about this stuff. Whether you're hiring, building something cool, or just want to geek out about tech, hit me up!

"Data is the new oil. It's valuable, but if unrefined it cannot really be used." ― Clive Humby

Thanks for checking out my profile! If you see a project you like, drop a ⭐ ― makes my day every time 😊