I'm Arin. I build systems that survive production.
Identity governance at Saviynt. Before that, four years of Ocean Engineering at IIT Madras (JEE top 0.2% of 1.1M). Heavy mathematics, computational modeling, physics. It trained me to reason about complex systems under extreme constraints. The systems just ended up being software rather than ships.
The ~20-hour job that hooked me
Large companies must continuously prove that no employee holds conflicting permissions. Creating and approving the same payment, for example. Running this check is called segregation-of-duties evaluation, and one large enterprise production workload ran about 20 hours at 65 GB. It re-examined the same organization hierarchy separately for every rule, thousands of times over. I replaced it with a single pass over the hierarchy plus bit-level permission comparison, where one processor instruction evaluates 64 people at once.
The remaining minutes are almost entirely database writes. Persisting the violation records is now the job, where evaluating them used to be. Making compute negligible didn't eliminate the bottleneck. It moved it somewhere cheaper. That migration is the shape of most performance work at scale.
That's the work I keep chasing: the right data structure collapsing a problem by two orders of magnitude.
What I work on
LICENSE INTELLIGENCE · LATEST
Companies pay SAP per user by access level, and overpay for dormant or over-privileged accounts. Most recently I built the inference engine that finds this waste over access data. Any number of accounts or identities, with dollar savings attached. It acts on what it finds, creating removal tasks with records that survive the nightly data refresh. Full story.
EXPORT PIPELINE
Streams reports of any size through a bounded buffer. Formatted Excel inside ZIPs directly to S3 via s3-outputstream (Apache-2.0, v2.0.0 on Maven Central): one reusable 5 MiB part buffer, explicit commit/abort semantics, real-S3 conformance receipt. Paginated reads → 50-row sliding window → S3 multipart → streaming ZIP. Full breakdown in this post.
AGENT SAFETY
When AI assistants hold tools, data access, and credentials, and can call other assistants or share credentials with them, dangerous permission combinations reappear with no person in the loop. Existing user-role checks didn't cover that setting, so I formulated the agent version and built the evaluation engine. Five risk patterns, deterministic checks, findings tracked to resolution. Full model · Technical report (PDF).
AUTOMATED FRAUD REVIEW
Software agents that investigate emergency-administrator logs on their own, within strict budgets on steps and time. Every log entry cited as evidence is re-checked against the source data; uncertain findings go to a human reviewer. The public research harness is open source with 110 tests and a frozen synthetic evaluation. Log-Insights-generator.
SECURITY INITIATIVES
Twice asked to join temporary company-wide security teams: categories of injection and access-control flaws across services, encrypted internal communication, test coverage from 30% to 80%.
AFTER HOURS · AI SYSTEMS
Nostos, a KV-cache-aware LLM router (radix-tree prefix affinity, write-up); ArinLM, a GPT built by hand with no model shortcuts; the same transformer in dependency-free C++ (gpt2-cpp, tested); and again in raw CUDA (in progress). Why three times.
How I got here without a CS degree
My department's timetable left no room for Data Structures, OS, or Databases. So I trained in programming contests against students who had taken those courses, and stayed until my rankings matched theirs. Four years of evenings after naval architecture labs. Progress was slow and nonlinear.
I learn the same way in ML: by rebuilding. A GPT by hand in PyTorch, then in dependency-free C++, then in raw GPU code; helmet detection with Faster R-CNN and YOLOv8; a Rubik's cube solver built on heuristic search with pattern databases.
Beyond engineering
Team Abhiyaan (IITM autonomous vehicles). I led external relations and in 2023 pitched to Finance Minister Nirmala Sitharaman and Commerce Minister Piyush Goyal at an IIT Madras Research Park event. Athletics: inter-department football runner-up, Dean's Trophy third place, hostel captain, IIT Madras co-captain in my final year.
What I'm working toward
I can build systems that work under extreme constraints. I've done it repeatedly at scale. But I've done it by inferring principles from operating production systems under pressure. I know that my ordered, replayable pipelines held up in review. I want the theory that explains why in the general case.
The current list (consensus, storage engines, verification, and serving intelligence cheaply) lives on the homepage, next to the production experience behind each item.