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shrutilekkala/README.md

About me

I'm Shruti, a software engineer who builds at the intersection of AI, backend systems, and product engineering.

I enjoy turning fuzzy ideas into dependable software: an interface people understand, an API that behaves predictably, an AI workflow that can be evaluated, and infrastructure that tells us when something goes wrong. My work spans agentic AI, LLM applications, distributed services, real-time products, data pipelines, and developer tooling.

shruti = {
    "building": ["AI-native products", "distributed backends", "developer infrastructure"],
    "cares_about": ["reliability", "clear system boundaries", "useful interfaces"],
    "currently_exploring": ["agent evaluation", "MCP", "observable AI systems"],
    "open_to": "software and AI engineering opportunities",
}

Tech constellation

Languages

Python, Java, C++, TypeScript, and JavaScript

Product & backend

React, FastAPI, Spring, Node.js, PostgreSQL, Redis, and Kafka

Cloud, infrastructure & ML

AWS, Azure, GCP, Docker, Kubernetes, Terraform, PyTorch, and GitHub Actions
More of my toolkit

LLMs · RAG · MCP · Agent orchestration · Evaluation · Guardrails · gRPC · WebSockets · SSE · PySpark · Airflow · dbt · Snowflake · OpenTelemetry · Prometheus · Grafana

Selected builds

Ask advertising questions in plain English and receive grounded results with supporting rows, citations, confidence, and explicit limitations.

React TypeScript FastAPI SQLite Docker

Privacy-conscious observability across request, retrieval, and model boundaries, designed to isolate failures rather than create new ones.

Python FastAPI Prometheus ASGI Docker

🧠 cppkv

A multithreaded C++ key-value server exploring sharded locking, worker pools, TTL, persistence, crash recovery, and performance tradeoffs.

C++17 POSIX sockets Concurrency CMake

A real-time visual workspace that keeps concurrent clients synchronized through WebSockets and a durable backend.

React FastAPI WebSockets PostgreSQL Redis

How I build

flowchart LR
    A[Useful product] --> B[Clear boundaries]
    B --> C[AI + data workflow]
    C --> D[Observable services]
    D --> E[Tests + evaluation]
    E --> F[Production confidence]

    style A fill:#9D83FF,stroke:#9D83FF,color:#07090D
    style F fill:#C9FF5A,stroke:#C9FF5A,color:#07090D
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  • Start with the user decision, not the trendiest framework.
  • Define retries, fallbacks, approvals, and recovery as part of the design.
  • Evaluate the full path: retrieval, model output, latency, cost, and operations.
  • Leave behind reproducible setup, tests, and honest technical tradeoffs.

Let’s build something useful.

AI systems should be impressive under real constraints—not only in a demo.



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