The short version, in full

Khalid Rizvi

Nearly four decades of building things that have to work.

Vienna, Virginia khalid.rizvi@icloud.com 703-656-6394 linkedin.com/in/khalidrizvi
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In brief

I'm a software architect with a long history of working on systems that quietly run in the background of consequential work: finance, healthcare, public-sector platforms, transportation, telecom. The early years were spent teaching database packages and writing dBASE, FoxPro, and Clipper code; the middle decades on C, C++, Forte, and Java; the modern years on Scala, TypeScript, JavaScript, and Go. Concurrent and parallel programming has been a quiet specialty, Go's CSP model in particular, where you share state by passing messages through channels rather than by sharing memory. Recent years have focused on Generative AI: production-grade LLM systems, retrieval pipelines, multi-agent orchestration, and the unglamorous but essential work of making models legible, observable, and useful. I keep an applied-research posture: read the papers, prototype the ideas, ship the ones that survive contact with reality.

How I work

Practice

Architecture, in service of the work

Aligning systems design with the actual goals of the people doing the work. I tend to ask what is essential, what is incidental, and what can be quietly removed without anyone missing it.

Modernizing without breaking

Most of my career has been spent moving older systems into newer ones. Mainframes to services, monoliths to microservices, batch to streaming, on-prem to cloud-native, without taking the business down for the weekend.

Generative AI, applied carefully

Building retrieval pipelines, agentic systems, and LLM-driven workflows for places where the answer matters. Most of the difficulty isn't the model. It's the surrounding scaffolding: evaluation, observability, prompt discipline, cost.

Working with engineers

I prefer small teams that read each other's code, argue well, and ship. I mentor when I can, and I've found that the best engineers tend to grow themselves once they're given problems they care about.

Calm under pressure

Some projects show up urgent: pandemic response, large migrations, regulatory deadlines. I've been around long enough to know that calm, clear thinking and a short feedback loop usually outperform heroics.

The work

Experience

Principal GenAI & Agentic Systems Engineer

  • Exploring the frontier of Agentic AI through hands-on experiments with multi-agent reasoning, self-reflection, and tool-use design patterns.
  • Building production-grade Retrieval-Augmented Generation systems with attention to retrieval quality, latency, and cost-per-token economics.
  • Researching emerging interoperability standards for foundation models, including Model Context Protocol (MCP), and applying them to real-world integration problems.
  • Engineering deterministic LLM behavior through prompt design, evaluation harnesses, and rigorous observability of model performance in production.
  • Translating R&D experimentation into resilient cloud-native deployments, with first-principles attention to system design, scalability, and developer experience.

Architect & engineering lead

  1. 2010s

    Cloud-native transformations for large enterprises in payments and transportation. Distributed computing, Go and Python microservices, real-time telemetry across fleets of physical assets. Concurrency by message-passing, Go's CSP via channels, became a default mental model.

  2. 2000s

    Multi-year IT modernizations for public-sector programs. Moved legacy monoliths to service-oriented architectures and automated rules engines, with measurable efficiency gains and fewer surprises.

  3. 1990s

    Java from 1996, alongside high-concurrency C-style systems for Fortune 500 clients in finance and healthcare. Zero-downtime requirements, the kind of work that taught me what "production-grade" actually means.

  4. Early 1990s

    Moved into C and C++ in 1993, then spent four years on Forte 4GL (1993 to 1997) building distributed enterprise systems before Forte was acquired and folded into the Java story.

  5. Late 1980s

    Started in 1987 teaching the database packages of the era (dBASE III, FoxBase, FoxPro, Clipper) and writing software for small businesses. The discipline of those tools, with their fixed records, careful indexing, and no margin for error, turned out to age well.

  6. Technological arc

    From dBASE batch updates to COBOL-to-services migrations to global loyalty platforms to predictive analytics for transit infrastructure. The names of the tools change; the underlying questions don't.

  7. Domain breadth

    Mission-critical systems across telecom, finance, public-sector, healthcare, and transportation. Mostly behind the scenes, and mostly fine.

The toolkit

Skills & technologies

Programming & Scripting

  • Python
  • Go
  • Java
  • Javascript
  • Typescript
  • Bash
  • SQL

Frontend Engineering & UI Systems

  • Full-stack UI via React, Next.js (SSR/ISR), and Go Templates
  • Enterprise State Management (Redux Toolkit, Context API, Hooks)
  • Modern Build Systems (Vite, Webpack) and Legacy Migrations (Angular/JS)
  • Performance Engineering (Code Splitting, Lazy Loading, Optimization)
  • Utility-First Design Systems (Tailwind CSS, Flowbite, Responsive UI)
  • Standards-Based Delivery (Semantic HTML5, WCAG, Cross-browser)

Cloud Platforms

  • AWS (Core Infrastructure, Serverless, and AI/ML via SageMaker & Bedrock)
  • Microsoft Azure (Cloud-Native Containers & Azure ML Ecosystem)
  • Google Cloud & Linode (Linux Workloads & Multi-cloud deployments)

Generative AI & Agentic Systems

  • Enterprise RAG
  • Agentic Orchestration
  • MCP Servers
  • Applied Research
  • Prompt Engineering
  • Conversational Chatbot

Big Data & Analytics

  • Apache Spark
  • Apache Kafka (large-scale data processing)
  • Tableau
  • Power BI (data visualization and dashboards)

Databases

  • MySQL
  • PostgreSQL
  • Oracle
  • MongoDB
  • DynamoDB
  • Elasticsearch
  • Redis

DevOps & MLOps

  • Docker
  • Kubernetes (EKS, AKS, ECS, Linode)
  • Jenkins
  • CI/CD pipelines
  • Infrastructure as Code (AWS CloudFormation, Terraform, AWS SAM)
  • Configuration management (Ansible)
  • End-to-end MLOps lifecycle (experimentation, versioning, production deployment, monitoring)

Security & Compliance

  • OAuth 2.0
  • mTLS encryption
  • JWT authentication

The foundation

Education

  • Master of Science in Computer Science

    California State University, Sacramento

    1995 to 1997

  • Master of Science in Mechanical Engineering

    NED University of Engineering & Technology

    1986 to 1988

  • Bachelor of Science in Mechanical Engineering

    NED University of Engineering & Technology

    1982 to 1986

Lately

Recent certifications

  • Talk to Your Documents with LangChain and Python

    08/2025

  • MCP Servers Made Easy with Python and OpenAI Agents

    07/2025

  • AWS Certified Cloud Practitioner

    08/2024
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