Senior engineer, 13+ years in distributed systems. I build anti-financial-crime systems at a Nordic bank by day, and solo-ship agentic-AI products — and win hackathons — by night.
I care about AI that explains its reasoning, leaves an audit trail, and actually works in production.
- 🏦 Software Engineer @ Swedbank — anti-financial crime & AML
- 🤖 Building agentic AI on Azure AI Foundry, A2A, and MCP
- 🧮 Researching GPU & quantum compute — q1729, the quantum taxicab
🛡️ ARGUSMulti-agent compliance intelligence Microsoft Agents League — AI Skills Fest 2026 · Reasoning Agents track ↗
problem> Manual KYC/AML review doesn't scale, and unaudited AI decisions don't survive a regulator's audit. approach> Five specialist agents coordinated over A2A on Azure AI Foundry. Every finding is cited via Foundry IQ. Full audit trail. All decisions are explainable, reproducible, and regulatory-proof. impact> 100% auditable, citation-grounded regulatory lookups replacing manual screenings.
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🏰 BASTIONA governed institutional-agent fleet for continuous access review All Things Agentic Hackathon 2026 · Fortified Enterprise Fleet track ↗
problem> Access review is quarterly work performed on continuously changing permissions. Automating the scan isn't enough — an institutional agent must remember prior human decisions, survive asynchronous retries, prove why it acted, and remain unable to turn suspicious input into a privileged write. approach> Read-only IAM review against the GCP project that runs it, including its own service identities. Deterministic code detects and scores findings; Gemini explains and routes already-minimized risk. Three institutional agents, one durable investigation identity — no raw IAM binding crosses the model or human-notification boundary. impact> Humans receive counts and allowlisted categories, never bindings. 161 tests at 100% statement and branch coverage.
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📡 DRIFTGPU & AI infrastructure release intelligence problem> Raw changelogs are noisy, unstructured, and full of false positives. Teams miss critical AI infrastructure updates. approach> High-precision release aggregation. Raw data → dependency checks → bounded, technical summaries. Built with FastAPI + pgvector semantic deduplication. impact> Converts raw, noisy changelogs into actionable release intelligence.
Code ↗ · Live app ↗ · API docs ↗ · Demo video ↗ · Devpost ↗ |
Durable incident memory for cold-started agents CockroachDB × AWS Hackathon 2026 — Build with Agentic Memory ↗
problem> Cold-started agents lose execution state. Multi-step workflows restart from zero, wasting compute and losing context. approach> Distributed checkpoint engine on CockroachDB. Restores 100% of execution state without pipeline restart. Mission-critical runtime guarantees. impact> Restores full execution state across distributed orchestration.
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Year-in-review intelligence for financial workflows Backblaze Generative Media · Devpost ↗
problem> Data-centric fintech teams want year-in-review insights. Existing tools are generic, not built for financial workflows. approach> Spotify Wrapped but for banking. Extracts transaction intelligence, generates insights, creates shareable year-end summaries.
Code ↗ · Live app ↗ · API docs ↗ · Demo video ↗ · Devpost ↗ |
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ARGUS: Compliance Infrastructure That Believes Financial Access Is a Human Right — techcommunity.microsoft.com · Guest post (July 2026)
Microsoft published my full write-up on the Educator Developer Blog, including how ARGUS coordinates five agents over A2A with citation-grounded risk scoring.
Lee Stott · Microsoft in #agentsleague (theme-aware image)
🧩 Agentic AI & Enterprise Intelligence I design AI systems with Azure AI Foundry, Foundry IQ, multi-agent orchestration, Agent-to-Agent (A2A) communication, and RAG with hybrid search — built to be explainable, grounded, and production-ready.
🏗️ Distributed Systems & Backend Architecture 13+ years building scalable platforms with Java (Spring Boot, Quarkus) and Python (FastAPI) — microservices, NoSQL, event-driven systems, and hybrid cloud across AWS, Azure, and OpenShift.
⚙️ AI Infrastructure, Performance & Compute I work at the infrastructure layer behind modern AI — GPU computing, NVIDIA CUDA, model serving, vector search, and performance engineering.
📡 Reliability, Observability & Platform Engineering I build resilient systems with Azure Functions, Service Bus, OpenTelemetry, Application Insights, and KQL — telemetry pipelines that hold up to enterprise-grade reliability and governance.
Languages & frameworks
Agentic AI & LLM
Cloud & infrastructure
AI infra, GPU & observability
2025 – Present · Software Engineer · Swedbank — Stockholm, Sweden Anti-financial crime · AML platforms · 95%+ test coverage across unified multi-module architecture
2021 – 2025 · Senior Java Developer · Viaplay Group — Stockholm, Sweden Media & streaming on AWS + Kubernetes · ~30% perf gains · ~40% delivery-speed acceleration
Mar–Sep 2021 · Software Developer · Expleo Technology Nordic — Gothenburg, Sweden Domain-driven microservices · ~50% faster onboarding via docs & workflow diagrams
2012 – 2021 · Senior Software Engineer · IBM — Sydney & Pune Regulated banking APIs for Westpac · ~25% response-time gains · Jenkins/Bamboo modernization
- q1729 — Ramanujan optimization via bare-metal GPU. CUDA-Q + cuQuantum + NVIDIA NIM Nemotron
- llm-qlab — LLM quantization benchmarks on consumer GPUs. Speed, VRAM, accuracy trade-offs
- pythonic-algorithms-lab — CPU vs GPU profiling with empirical Big-O analysis. CuPy + Numba CUDA
- iq-series — Hands-on Microsoft IQ notebooks. Foundry IQ, Work IQ, Fabric IQ
- NVIDIA Certified Professional: Agentic AI — NVIDIA
- Machine Learning and AI Foundations — LinkedIn Learning Community
- IBM Machine Learning Essentials — IBM
- Python for Data Science — IBM
- Kubernetes Administration — The Linux Foundation
Full certification list on LinkedIn ↗
Building trustworthy AI systems that explain their reasoning, leave an audit trail, and actually work in production.
If that's the kind of problem you're working on — I'd love to talk.
github.com/iarjunganesh · arjunganesh.dev