Full-StackAppliedAI Engineer
I design end-to-end AI systems — from autonomous agents and computer vision models to the MLOps pipelines that keep them reliable in production.
↓Scroll to exploreSelected Work
( 08 Projects )Accessibility Audit Agent
Autonomous WCAG 2.2 Auditor
Confidential · OnFinance AIMetering & Entitlements
LLM FinOps Platform
Confidential · OnFinance AIReporting & MIS
Regulatory Disclosure Workflow
Confidential · OnFinance AIPlatform & GitOps Delivery
ComplianceOS Cloud Infrastructure
Confidential · OnFinance AIOneARVO Vision
Anti-Counterfeiting & MLOps Platform
Confidential · OneARVO Ventures
( About )
I build autonomous AI agents — and the production systems that keep them reliable, governed and scalable.
I'm a Full-Stack Applied AI Engineer. I design end-to-end systems that pair LLM-driven agents and computer-vision models with the backend that makes them production-grade — orchestration, usage metering, observability and MLOps.
Recently I've built multimodal agentic pipelines on LangGraph (deterministic checks fused with LLM vision), an LLM token-metering and entitlements platform for cost governance, and retrieval-augmented document systems — owning the full lifecycle from research and modelling to real-time deployment, resilience and monitoring.
( Education )
B.Tech, Information Technology
ABES Institute of Technology (ABESIT) — 2024
Focused on deep learning and computer-vision research, and served as Treasurer of the Bit-Brain Club.
( Toolkit )
Experience
( Career )Applied AI Engineer
OnFinance AI
Generative-AI compliance platform (ComplianceOS) for the BFSI sector.
Bengaluru, IN
Feb 2026 — Present
- Architected an autonomous WCAG 2.1/2.2 (Level A & AA) accessibility-audit agent — a 14-node LangGraph pipeline that audits Android apps across 56 success criteria by fusing deterministic pixel math (contrast, target-size), accessibility event-log parsing, and multimodal LLM vision (screenshot + UI hierarchy).
- Scaled that agent to production: adaptive async concurrency (10→25 workers by load), a circuit breaker, per-node checkpoint/resume, and a fire-and-forget S3 watcher daemon (Docker / Kubernetes) that auto-dispatches one audit per capture session.
- Cut false positives with confidence gating, two-pass LLM verification, and hierarchy pre-filters that remove ~60% of wasted vision calls; cross-screen dedup collapses repeated defects into one canonical finding, with a per-session LLM cost ledger for FinOps.
- Built an LLM usage-metering & entitlements platform — a stateless Go 1.23 microservice bridging the compliance agents to OpenMeter for per-customer / per-agent / per-model token metering, quotas, RPM/TPM rate limits and billing-readiness, backed by Postgres (pgx).
- Engineered the metering platform for resilience: fail-open admission (a metering outage never blocks an agent run), a durable Redis outbox with at-least-once delivery and idempotent dedup, and Postgres advisory locks serializing usage across replicas — surfaced through a React + Vite + Recharts admin console behind Dex/OIDC.
- Delivered the recurring regulatory Reporting & Disclosure (MIS) module — table + calendar tracking of periodic filings with due dates, frequency, owners and SPOC, scoped per business unit and regulator and traced to source circulars, backed by MongoDB + Qdrant semantic search.
- Owned GitOps delivery for ComplianceOS on EKS — Kustomize overlays reconciled by ArgoCD across staging, UAT and prod; exposed and OIDC-secured the metering console via ALB + ACM TLS + Dex, with secrets pulled at runtime from AWS Secrets Manager (External Secrets) and Namecheap DNS pointed at the shared ALB.
- Hardened and operated platform services: environment-locked metering so shared OpenMeter + Postgres never cross-contaminate across envs, removed the admin API from the edge (proxied in-cluster), and shipped a RabbitMQ checklist pipeline (poller → agent → Bedrock via LiteLLM) plus an investigation views-refresh service with health probes, spot scheduling and read-only root filesystems.
Machine Learning Engineer
OneARVO Ventures
Noida, IN
Dec 2024 — Feb 2026
- Built Vision Transformer and contrastive-learning models on 200K+ images for QR and copy-detection-pattern verification, reaching 97.9% accuracy.
- Designed automated data curation and perception pipelines to detect, crop and augment QR regions for large-scale training on AWS SageMaker.
- Architected end-to-end ML pipelines with distributed training, hyperparameter tuning and experiment tracking via SageMaker, MLflow and Airflow.
- Built real-time inference with model versioning and monitoring, cutting average prediction latency by 65%.
- Implemented MLOps and observability — CI/CD, drift monitoring and feedback loops with GitHub Actions, CloudWatch and Grafana.
Flutter Developer
BlueTrans
Noida, IN
May 2024 — Nov 2024
- Integrated the Razorpay payment gateway into an Android app for secure transactions, improving successful payments by up to 70%.
- Designed app UI to enhance user experience and engagement.
- Implemented authentication, improving security and reducing unauthorised access.
- Increased app speed by 30% for a smoother experience across 400 daily active users.