I engineer AI systems that are fast, measurable, and production‑ready.
Fast pipelines. Rigorous evals. Backends that hold under load. MS Computer Science at Texas Tech (GPA 3.8), building at the intersection of applied AI and production systems — from sub-100ms RAG to concurrent async inference at scale.
RAG Pipeline Latency
NetSec Arcade — 1,000+ indexed chunks
Semantic Grading Accuracy
Embedding-based, no fine-tuning
Async Throughput Gain
Non-blocking APIs & pipelines
Concurrent Tasks
FastAPI + Redis + SSE, zero blocking
FLOW
AI-powered daily life optimizer — optimizes a full human day
A personal AI that reads your morning state, pulls your calendar, and generates a realistic full-day plan balancing work, body, and relationships. Gets smarter over time through daily feedback loops — like having a life strategist that actually knows your schedule.
IMPA Product Matcher
Intelligent semantic search over 1M+ marine supply products mapped to the global IMPA catalog
An AI/ML layer for a centralized marine supply platform — hybrid retrieval combining vector search and Elasticsearch to automatically map product descriptions to correct 6-digit IMPA codes with ≥96% Top-3 accuracy. Like having a procurement expert who's memorized the entire IMPA catalog.
What I Do
Systems
Design and build production-grade backend systems that are fast, reliable, and scalable. From async APIs to distributed pipelines, I focus on architectures that hold under real-world load.
AI / ML Engineering
Develop end-to-end AI systems — from RAG pipelines to evaluation frameworks and LLM-powered applications. Not wrappers, but systems designed for measurable performance and real use cases.
Automation & Workflows
Build intelligent automation pipelines that reduce manual effort and improve system efficiency. From event-driven workflows to agentic task orchestration, I design systems that operate reliably with minimal intervention.
Selected Work
Experience
Aug 2024 – Jun 2025
Texas Tech University, WCOE
Lubbock, TX · Part-time
Graduate Research Assistant — Software Engineering Lead
- —Led teams building production backend platforms with async execution and concurrent workloads, improving iteration speed by 30%+.
- —Designed non-blocking APIs and background task pipelines, reducing latency and enabling scalable concurrent task handling.
- —Mentored engineers on data structures, concurrency models, and clean system abstractions.
Jul 2023 – Jul 2024
Rakuten Symphony
Indore, India · Full-time
Software Engineer
- —Built and enhanced monitoring pipelines for distributed telecom network systems, reducing incident detection time by ~30%.
- —Applied early-stage AI techniques to identify failure patterns — enabling proactive issue detection and reducing recurring incidents.
- —Improved mean time to resolution (MTTR) by ~20% through root-cause analysis tooling across cloud-native Open RAN infrastructure.
Mar 2023 – Jun 2023
Rakuten Symphony
Indore, India · Internship
Software Engineering Intern
- —Analyzed UAT and regression defect trends across cloud-native network modules, reducing post-release issues by 40%.
- —Built structured defect analysis pipelines and release validation reports used for QA and engineering go/no-go decisions.
Feb 2022 – Apr 2022
Cylsys Software Solution
Mumbai, India · Internship
Software Engineering Intern
- —Developed Node.js backend services with normalized MySQL schemas for healthcare data workflows.
- —Implemented reusable APIs and components, reducing repeated operations by 25%+.
Education
Master's in Computer Science
Texas Tech University·GPA 3.8
AI systems, distributed systems, production ML
B.Tech in Computer Science
IPS Academy·GPA 3.74
Core CS, algorithms, system design
Skills
Applied AI & ML
ML Frameworks
Backend & Systems
Languages
Infrastructure
Frontend
Building something with AI? Let's talk.
Open to ML/AI engineering roles — May 2026. I bring fast pipelines, rigorous evals, and backends that hold under load.
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