name: "Zaid Alvi"
role: "Software Engineer • AI Engineer • Full Stack Developer"
focus: "Building enterprise-grade, intelligent, scalable products"
philosophy: "Ship with precision. Engineer with intent. Design for scale."I am a Software Engineer driven by first-principles thinking and a relentless pursuit of engineering excellence. My work sits at the intersection of Artificial Intelligence, Full Stack Development, and Product Engineering — where scalable architecture meets intelligent, human-centric experiences.
I care deeply about system design, performance engineering, developer velocity, and product outcomes. Whether shipping distributed backends, training deep learning models, or crafting pixel-perfect frontends, I approach every problem with the mindset of an owner building for the long term.
Open To: Software Engineering Roles • AI / ML Engineering • Full Stack Positions • Research Collaborations • Impactful Open Source
Languages
Frontend
Backend & Databases
Cloud, DevOps & Tooling
AI / ML Ecosystem
| Domain | Proficiency | Details |
|---|---|---|
| Machine Learning | ██████████ Expert | Supervised, Unsupervised, Ensemble Methods, Model Optimization |
| Deep Learning | █████████░ Advanced | CNNs, RNNs, Transformers, Attention Mechanisms, Fine-Tuning |
| Natural Language Processing | █████████░ Advanced | LLMs, RAG, Embeddings, Semantic Search, Prompt Engineering |
| Computer Vision | ████████░░ Proficient | Object Detection, Segmentation, OCR, Image Classification |
| MLOps | ████████░░ Proficient | Model Deployment, Monitoring, CI/CD for ML, Feature Stores |
| Generative AI | █████████░ Advanced | LangChain, LlamaIndex, Vector DBs, Multi-Agent Systems |
| Data Engineering | ████████░░ Proficient | ETL Pipelines, Streaming, Data Lakes, Warehousing |
🟣 Project Alpha — Enterprise AI Platform
A production-grade AI orchestration platform enabling teams to build, deploy, and monitor LLM-powered applications at scale with enterprise-grade observability and cost governance.
| Attribute | Details |
|---|---|
| Stack | Next.js • FastAPI • PostgreSQL • Redis • LangChain • Pinecone |
| Scale | 10M+ inference requests/month • 500+ tenants |
| Performance | P99 latency < 220ms • 99.98% uptime |
| Security | SOC2-ready • End-to-end encryption • RBAC + SSO |
| Impact | Reduced LLM ops overhead by 68% for enterprise teams |
| Repository | → View Code |
Engineered a modular, multi-tenant architecture with event-driven microservices, vector-native retrieval, and real-time observability. Designed with horizontal scalability, zero-trust security primitives, and a plug-and-play model provider interface.
🟣 Project Nova — Real-Time Analytics Engine
A high-throughput real-time analytics engine capable of ingesting millions of events per second and delivering sub-second insights across a distributed dashboard layer.
| Attribute | Details |
|---|---|
| Stack | Go • Kafka • ClickHouse • React • Kubernetes • gRPC |
| Scale | 2M events/sec sustained ingestion |
| Performance | Query response < 150ms on 10B+ rows |
| Security | mTLS • Zero-trust ingress • Audit logging |
| Impact | Powered mission-critical dashboards for 40+ enterprise clients |
| Repository | → View Code |
Built a columnar-store-backed streaming pipeline with a custom query planner and materialized view engine. Optimized for cost, throughput, and freshness with intelligent tiered storage.
🟣 Project Vertex — Full Stack SaaS Product
A modern SaaS product delivering enterprise-grade workflow automation with an intuitive drag-and-drop builder, deep integrations, and AI-assisted authoring.
| Attribute | Details |
|---|---|
| Stack | Next.js 14 • NestJS • PostgreSQL • Prisma • Stripe • AWS |
| Scale | 15K+ active users • 200+ integrations |
| Performance | Lighthouse 98+ • TTI < 1.2s |
| Security | OAuth2 • JWT • RLS • GDPR compliant |
| Impact | Automated 3M+ workflows • 45% adoption growth QoQ |
| Repository | → View Code |
Architected a serverless-friendly monolith with domain-driven boundaries, event sourcing for critical flows, and a design system built for accessibility and velocity.
🟣 Project Helix — Computer Vision Pipeline
A robust computer vision pipeline for large-scale document intelligence, extracting structured data from unstructured visual documents at scale.
| Attribute | Details |
|---|---|
| Stack | PyTorch • FastAPI • ONNX • Triton • Redis • S3 |
| Scale | 5M+ documents processed/month |
| Performance | 92% F1 on production benchmarks |
| Security | PII redaction • Encrypted at rest & in transit |
| Impact | Eliminated 12K+ manual hours across operations |
| Repository | → View Code |
Designed a modular OCR + layout-understanding + downstream-classifier pipeline with hardware-aware inference optimization and continual learning loops.
Jan 2024 — Present
Driving end-to-end product engineering across backend services, AI-enabled features, and developer platforms — with a strong bias toward reliability, performance, and long-term architectural quality.
- Architected and shipped core platform services serving 10M+ requests/day
- Led AI feature engineering across LLM, RAG, and personalization workflows
- Reduced infra costs by 32% through query optimization and caching strategy
- Established CI/CD, observability, and on-call standards for the engineering org
- Mentored engineers on system design, code quality, and production readiness
Python TypeScript Go AWS Kubernetes PostgreSQL Redis LangChain
Jun 2023 — Dec 2023
Owned the design and delivery of production ML systems from data pipelines to model serving, with measurable business impact across recommendations, NLP, and vision use cases.
- Built and shipped recommendation models improving CTR by 21%
- Fine-tuned transformer-based NLP models for intent classification
- Designed feature stores and ETL pipelines for large-scale training workloads
- Deployed models with Triton + FastAPI with autoscaling on Kubernetes
PyTorch TensorFlow FastAPI Airflow Kafka Docker GCP
| Recognition | Details |
|---|---|
| 🏆 Hackathon Winner | 1st place — National-level 36-hour AI Hackathon (100+ teams) |
| 🥇 Top Contributor | Recognized open-source contributor across ML tooling ecosystem |
| 🎖️ Coding Championship | Finalist — Inter-collegiate competitive programming contest |
| 📜 Research Publication | Co-authored peer-reviewed paper on applied deep learning |
| ⭐ GitHub Growth | Repositories collectively earning 500+ stars from the developer community |
| 🎓 Academic Honors | Consistent top academic standing • Merit-based scholarship recipient |
| 🚀 Product Impact | Shipped features used by 100K+ end users in production |
Amazon Web Services
Oracle
NPTEL
Cisco
learning:
- Distributed Systems @ Scale
- Advanced LLM Architectures & Agentic Workflows
- High-Performance Rust for Systems Engineering
building:
- Enterprise AI Infrastructure Platforms
- Real-time Multi-Agent Systems
- Developer Tooling for ML Workflows
exploring:
- Vector Databases & Retrieval Systems
- MLOps & Model Observability
- Edge AI & On-Device Inference
open_to:
- Software Engineering Roles (SDE / SDE-II)
- AI / ML Engineering Positions
- Research Collaborations
- Open Source Contributions
- Speaking & Technical Writing Opportunities
"Great engineering is invisible — it just works, at scale, under pressure, over time."