Enterprise AI Automation
End-to-end automation of complex operational workflows — document processing, classification, validation, and decisioning — engineered to run reliably in production.
AI Infrastructure Engineer
Building Reliable
AI Infrastructure
for Enterprise
Operations
I design and engineer AI systems that automate complex operational workflows using modern LLM infrastructure, orchestration, APIs, and cloud-native architectures.
Physics Graduate.AI Infrastructure Engineer.Systems Thinker.
Most AI developers start with a model and hope the system holds. I start with the system. A background in physics taught me to reason about complexity, uncertainty, and failure before writing a single line of code — so what I build is designed to stay reliable long after the demo ends.
- M.Sc. Physics
- Python
- FastAPI
- LangGraph
- Temporal
- Production-first Engineering
- Enterprise Architecture
- Cloud-native Systems
Trusted Technologies
A modern, production-grade toolchain
The frameworks and platforms I rely on to ship reliable AI infrastructure at enterprise scale.
- Python
- FastAPI
- LangGraph
- Temporal
- Docker
- PostgreSQL
- AWS
- OpenAI
- Anthropic
- OpenRouter
What I Build
Systems that turn AI into operational leverage
Three pillars underpin everything I ship — designed together as one dependable platform.
LLM Infrastructure
Robust foundations for language models: provider routing, prompt and context pipelines, evaluation, observability, and cost controls built to scale.
Workflow Orchestration
Durable, long-running processes coordinated across people, services, and models with retries, state, and full auditability at every step.
Services
End-to-end AI infrastructure for your operations
From first discovery call to a maintained production system — engineering support at every stage.
AI Workflow Automation
Automate repetitive, high-volume operational processes with orchestrated AI pipelines that stay accurate under real-world load.
Custom AI Systems
Purpose-built systems designed around your data, constraints, and domain — not generic wrappers around a single model.
Internal AI Assistants
Secure, context-aware assistants that give your teams instant answers grounded in your own documents and tools.
Enterprise Integrations
Connect AI into the systems you already run — ERPs, CRMs, ticketing, and internal APIs — with reliable, observable pipelines.
Infrastructure Consulting
Architecture reviews and technical strategy for teams scaling AI from prototype to dependable production infrastructure.
Backend APIs
Production-grade services and APIs engineered for performance, security, and long-term maintainability.
How I Work
A disciplined path from idea to production
A repeatable engineering process that removes guesswork and keeps stakeholders aligned throughout.
- 01
Discovery
I map your operations, data, and constraints to find the highest-impact automation opportunities before writing a line of code.
- 02
Architecture
A clear technical blueprint — models, orchestration, integrations, and infrastructure — designed for reliability and scale.
- 03
Development
Production-grade engineering with evaluation, observability, and tests baked in from the first commit.
- 04
Deployment
Cloud-native rollout with monitoring, safeguards, and CI/CD so the system ships confidently and stays healthy.
- 05
Optimization
Continuous tuning of accuracy, latency, and cost — turning a working system into a durable competitive advantage.
Tech Stack
Modern. Scalable. Reliable.
A carefully chosen stack that balances developer velocity with production dependability.
Languages
- Python
- TypeScript
- SQL
- Bash
Frameworks
- FastAPI
- LangGraph
- Next.js
- Pydantic
Infrastructure
- Docker
- Temporal
- Kubernetes
- GitHub Actions
Databases
- PostgreSQL
- pgvector
- Redis
- S3
Cloud
- AWS
- Vercel
- Cloudflare
- Terraform
AI
- OpenAI
- Anthropic
- OpenRouter
- Embeddings
About
Engineer. Problem solver. Systems thinker.
I'm Vijay Jangir, an AI Infrastructure Engineer focused on building intelligent systems that automate complex operational workflows for logistics and mid-sized enterprises. I care less about demos and more about systems that keep working long after launch.
My philosophy is simple: understand the problem deeply, design for reliability first, and treat AI as infrastructure — measurable, observable, and maintainable — rather than a black box.
- Mission
- Make enterprise operations faster, more accurate, and less costly through dependable AI infrastructure.
- Approach
- Systems-first engineering — model behavior, orchestration, and integrations designed as one coherent platform.
- Foundation
- A background in Physics and software engineering, applied to reasoning rigorously about complex, uncertain systems.
Why Work With Me
Built to the standard enterprises expect
The difference between a working prototype and infrastructure you can depend on.
Physics-driven thinking
A scientific foundation means I reason from first principles — modeling uncertainty and edge cases instead of hoping they never happen.
Enterprise engineering
Security, observability, and compliance are designed in from day one, so systems hold up to real enterprise scrutiny and scale.
Long-term maintainability
Clean architecture and documented, testable code mean your infrastructure stays reliable — and adaptable — for years, not weeks.
FAQ
Questions, answered
A few things clients ask before we start working together.
Why It Matters
Why AI Infrastructure Matters
Most AI projects don't fail because of the model.They fail because of unreliable infrastructure.
- Reliable orchestration.
- Reliable context.
- Reliable evaluation.
- Reliable deployment.
That's what I build.
Let's Connect
Let's Build Infrastructure That Scales.
Tell me about your operations and automation goals. We'll find where AI infrastructure can create real, measurable impact.