AI Product Manager at Culinda Inc. who builds as much as she plans.

I work at the intersection of product vision, full-stack AI engineering, and agent systems.
I don’t just talk about AI products. I design them, ship them, and iterate in public.

• AI agents (single + multi-agent)
• Tool-calling, memory, and workflow orchestration
• Agent-backed products, not just notebooks
• FastAPI-powered backends for AI services

• Java-first mindset with modern AI stacks
• APIs, services, and integrations that scale
• End-to-end AI features: backend → product → UX
• Practical ML, not academic-only experiments

• Data curation & pipelines
• Model experimentation and evaluation
• Turning ML outputs into usable product features

I practice builder-style product management:

• Product vision & strategy for AI-native products
• Translating fuzzy AI capabilities into clear user value
• PRDs that engineers actually want to read
• Rapid prototyping to validate product bets
• Shipping > slides

I believe great AI PMs:

• understand the tech deeply
• can reason about trade-offs
• and are comfortable getting their hands dirty in code

• Languages: Java, JavaScript, Python
• Backend: FastAPI, REST APIs
• AI/ML: LLMs, agent frameworks, applied ML
• Systems: Distributed systems, cloud-native apps
• Frontend: Full-stack integrations for AI features

• Cloud computing & distributed systems projects
• Applied ML and data work
• Full-stack AI experiments
• Product-driven repositories (not toy demos)

Most repos here exist because:

“I wanted to understand how this actually works in a real product.”

I enjoy conversations about:

• AI product management tools & workflows
• Building AI products that users trust
• Agents in production vs theory
• Where PMs should (and shouldn’t) use AI

If you’re building something interesting in AI, product, or systems — happy to connect.

📍 AI PM @ Culinda Inc.
🔗 https://shrashti.com