Tarun builds systems that ship.
I build production AI systems with .NET, Azure and Angular — LLM features that cite their sources, fail safely, and ship through pipelines that can be rolled back.
Software Engineer — AI Systems, .NET & Azure
Hyderabad, India · works worldwide
AI engineering is more than a model call. Evidence, evaluation, fallbacks and deployment are part of the product — because a model is only useful when people can rely on it.
Currently building
- AI in production
Shipping LLM features inside a live .NET finance platform — structured outputs, prompt caching, and queries written for the database that actually runs them.
- Public AI work
Release-risk review, grounded retrieval and evaluation gates — public repositories with tests and stated limits.
- Azure delivery
CI/CD, observability and rollback paths that carry AI changes to users without surprises.
01 / Selected work
Selected work
A flagship AI build, public experiments, production software, and a small collection of sites I make for people.
Featured AI Engineering
The flagship: a working release-risk review MVP — public, tested, and explicit about what the model does and does not do.
- Aegis Release Guard — MVP
AI-assisted deployment risk reviewMVP
Aegis Release Guard
A working MVP that turns a git diff into cited release risks, focused test suggestions and a rollback checklist.
LLM analysis with a deterministic fallback: runs end to end with no external AI required, xUnit + Vitest suites, CI and Docker in the box.
- .NET 10
- React
- TypeScript
- xUnit
AI Engineering Experiments
Public lab work. Each repository states exactly what works today and what is deliberately left out.
- TraceRAG — Concept
Citation-first retrieval scaffoldConcept
TraceRAG
A self-checking .NET retrieval baseline that answers from local evidence, cites exact chunks and refuses unsupported questions.
Every answer is backed by document- and chunk-level citations; unsupported questions return "insufficient evidence" rather than a guess.
- .NET 10
- Minimal API
- Retrieval
- xUnit
- IncidentSight — Concept
Multimodal incident-triage scaffoldConcept
IncidentSight
A safety-first FastAPI baseline that extracts log evidence, inspects image metadata and keeps every remediation step human-approved.
36 tests over a typed, validated pipeline; every remediation step is gated behind human approval and nothing runs automatically.
- Python
- FastAPI
- Pydantic
- Pytest
- ModelLedger — Concept
Offline LLM regression gatesConcept
ModelLedger
A working TypeScript CLI scaffold that compares recorded model outputs and fails CI when quality, latency or cost gates regress.
Deterministic quality, latency and cost gates that fail CI with a non-zero exit code the moment a recorded output regresses.
- TypeScript
- Node.js
- Zod
- Vitest
Selected Work
Production software for real customers. Details are kept general where confidentiality requires it.
- Safe Financials — Shipped
AI inside production finance softwareShipped
Safe Financials
Integrating LLM-driven features into an established .NET financial platform with attention to compatibility and failure handling.
LLM features running inside a live .NET / SQL Server platform — structured outputs, prompt caching, and query patterns written for the production compatibility level.
- .NET
- EF Core
- SQL Server
- Claude API
- Angular
- AmpleLogic — Shipped
Enterprise software engineeringShipped
AmpleLogic
Backend, frontend and deployment work on enterprise software for regulated-industry workflows.
- .NET
- Angular
- SQL Server
- CI/CD
Portfolios
Sites I design and build for photographers — a frame as considered as the pictures. Both live.
Fashion photographer · HyderabadShipped
Sandeep Sanka
A cinematic, image-first site for an established Hyderabad fashion photographer whose editorial and campaign work includes Allu Arjun, Vijay Deverakonda, Rashmika Mandanna, and houses such as RWDY.
Live at sandeepsanka.com — a full-bleed, motion-paced editorial site built with Next.js, TypeScript and GSAP.
- Next.js
- TypeScript
- GSAP
Celebrity & portrait photographer · BLR–HYDShipped
Sujana
A cinematic, film-still portfolio for a freelance photographer shooting celebrity portraits, actor portfolios, brand campaigns, and pre-weddings between Bengaluru and Hyderabad.
Live at sujana.zip — a monochrome, film-still portfolio built with Next.js and TypeScript.
- Next.js
- TypeScript
- CSS
02 / About
About
Portrait, drawn from live data — the loom remembers your movement.
A software engineer specializing in practical AI integration and the systems around it.
My day-to-day work crosses .NET services, EF Core and SQL Server data layers, Angular and TypeScript interfaces, Azure delivery, and LLM-powered product features. I care about grounded outputs, explicit failure modes and what happens after the demo reaches production.
My production experience includes fintech software at Safe Financials and enterprise systems at AmpleLogic. The public projects below show the same approach in the open: working code, tests, and honest statements of what each one does not do.
I also design and build personal sites. The Portfolios collection is that side of the practice: a live photographer’s site and an editorial personal portfolio, composed as custom work rather than a template.
- Based
- Hyderabad, India
- AI
- LLM integration · RAG · evaluation · guardrails
- Engineering
- .NET · C# · Angular · TypeScript · SQL Server
- Delivery
- Azure · CI/CD · observability · rollback design
03 / Capabilities
Capabilities
The parts of AI development that turn a promising prototype into software a team can inspect, test and operate.
- 01
AI Product Engineering →
Choosing where a model belongs, designing structured contracts around it, and building explicit fallbacks instead of treating the provider as magic.
- 02
Retrieval & Agents →
Citation-first retrieval, tool boundaries and human approval paths that keep AI systems useful without hiding uncertainty.
Exploring: MCP · Agent workflows
- 03
Evaluation & Safety →
Regression gates, evidence checks, privacy boundaries and adversarial cases that measure behavior before an AI change ships.
- 04
Production Systems →
ASP.NET Core services, Angular and TypeScript interfaces, SQL data layers, Azure delivery, telemetry and recovery plans built for the environment that actually runs them.
04 / Contact
Build AI people can trust.
Open to full-time roles and product engineering work — especially teams shipping AI features that have to hold up in production.