Venkatesh Surve

End-to-end owner of Patronus's technical stack — designed, built, and deployed from scratch. The Consent Management Platform now handles 8 million user consents across 97 million banner interactions, with sub-50ms write latency and 9 months of 100% availability via an event-driven, immutable-log architecture. Latency-critical paths — including a PII vault for structured personal data storage and retrieval — were built in Golang, where Python's overhead wasn't acceptable. The harder problem was what came after: building an AI-First privacy engine where autonomous agents interpret regulations, assess data flows, and flag compliance gaps — with a specific focus on India's DPDP Act alongside GDPR and CCPA. The flagship piece: a DPDP-compliant browser automation agent built on a fine-tuned Gemma 4 (31B) model, trained on 12,000 pages of user journeys and compliance data. A 3-layer context compression architecture (semantic pruning, delta compression, full compaction) achieves 5–10x token efficiency vs standard browser agents. On an internal benchmark against frontier API models, the fine-tuned model ranked first, at lower latency and cost. The DPIA Agent conducts impact assessments and surfaces compliance gaps for human sign-off, cutting turnaround from 3 weeks to 1 week. The goal was not to replace legal judgment but to eliminate the slow, manual parts of the loop entirely.

Built a serverless RAG-based document intelligence pipeline using LangChain and AWS Lambda, managing a terabyte-scale vector index in self-hosted Elasticsearch. The bottleneck was never the LLM — it was retrieval latency. Solved it by going deep into Elasticsearch internals and vector embedding dimensionality: tuning sharding strategies, memory mapping, and where the pipeline was wasting cycles. Queries that took 15 seconds came down to 2–3. The pre-processing problem was equally non-trivial: built a high-throughput Rust microservice wrapping pdfium, tuned specifically to the structural variance of our document corpus — off-the-shelf extraction pipelines couldn't handle it at terabyte scale without choking. Measurable throughput gains over the Python baseline. Real-time document interaction demands real-time retrieval.

Containerized a legacy Django monolith into a microservices architecture. The real work was in Celery: refactoring workers for genuine parallel execution rather than the illusion of it. The same 100GB of unstructured data — images, documents, presentations — went from 13 hours to 4. Getting distributed task queues to behave correctly under load is a different problem from getting them to run at all. Also built document intelligence across 8 file types using Elasticsearch for indexing and switched from Tesseract to PaddleOCR for meaningful accuracy gains on dense document layouts.

Freelance backend work running in parallel with Keytech, community modding, and competitive overclocking — before transitioning into full-time employment. Django stack: APIs, data pipelines, integrations. Real client work is a different discipline from side projects; timelines are fixed and ambiguity is expensive.