Career Changelog
All notable changes to my career
Format inspired by Keep a Changelog; each role is a semantic-version release. For the polished narrative version, grab the PDF resume.
- 4.0.0 Technical Team Lead — GenAI & Computer Vision@ VCTI (Client: Cisco) · Bangalore, India · Nov 2022 → Present
Leading AI agent platforms for Cisco optical networking — shipping the first MCP-protocol AI tool server in CONC 26.1.1, the AINetOps (C-ONIDAA) product line, and a suite of Generative-UI dashboards. Owning architecture, security, delivery, and team enablement across 10+ engineers on parallel workstreams.
- Python
- LangGraph
- LangChain
- MCP
- FastAPI
- React
- Docker
- Kubernetes
- AWS EKS
- mem0
- Docling
- EFK
### Added
- + CONC MCP Server (26.1.1): Led delivery of the first MCP-protocol AI tool server for Cisco optical networking — positioned as a standard microservice in the NxF framework.
- + AINetOps (C-ONIDAA) Beta: Owned end-to-end development and early field-trial delivery (FastAPI, LangGraph, React, MCP, Docker).
- + Deep Agent for Automated RCA: Architected a LangGraph workflow that autonomously investigates customer incidents — traverses logs and process flows, generates L1 triage reports, and reduces MTTR by 50%.
- + Agentic documentation pipeline: AutoGen + LangGraph system that turns meeting transcripts into technical docs, reducing manual effort by 90%.
- + Production RAG: LlamaIndex contextual RAG over private enterprise data hitting 80% context recall via custom embedding fine-tuning.
- + Self-hosted Generative UI: Interactive charts/tables in chat without external LangSmith dependency — a first in the CONC ecosystem. POC → production dashboards (NHot, Triage, Alarm, Utilization) in 8 weeks.
- + Manager's Triage Dashboard: Generative-UI dashboard collating critical data sources with interactive deep-dive views.
- + NHot: Course-generation platform for new Cisco interns and internal employees.
- + Open-source optical encoder: Guided team to publish the first open-source encoder for optical networking on Hugging Face.
- + Cisco Optical AI Agent + CONC MCP client: Built and delivered for the PONC demo.
- + Cross-platform agent integration: Atlassian, ELK, and CDETS tools wired into agents for cross-platform intelligence.
- + Persistent agent memory: Established
mem0-based recall across sessions supporting multi-turn Q&A for complex troubleshooting. - + Document conversion: Docling-based pipelines and a doc-to-doc agent for higher structural fidelity.
- + Reusable LLM tooling patterns: FunctionTool wrappers, tool cataloging, consistent descriptions.
- + MLOps: Migrated inference pipelines to AWS EKS; GPU optimization reduced inference latency and cloud costs; standardized Docker workflows for CI/CD; EFK stack for observability.
- + Computer Vision: Improved pole-detection accuracy by 48% using DeepLabv3+ semantic segmentation on satellite imagery.
- + Security-first culture: API lockdown, Nginx rate limiting, JWT auth, OWASP review, DDoS protection, CSDL compliance — completed before any external deployment.
- + OFC Demo: Made AI products demo-ready and deployed for Cisco's international OFC showcase.
- + Team enablement: Trained the AI team on LangGraph, MCP, LangChain, and agent fundamentals; scaled output across 10+ engineers on parallel workstreams (CSDL, dashboards, token optimization, DeepEvals, Deep Agents, SSO).
- + Cutting-edge adoption: Continuously evaluated and incorporated MCP protocol, Generative UI, Deep Agents, and DeepEvals into production.
- + Feature POCs delivered: Gen UI, SMX evaluation, SSO, multi-agent, RCA deep agents, MCP orchestration.
- + AI pole measurement methodology: Decomposed the problem and provided optimal solutions for each part.
- + In-house GPU infrastructure: Set up GPU machine with full AI stack for the team.
### Changed
- ~ VCTI ↔ Cisco alignment across Core Platforms, COSM, CONP, and NCS2K legacy; built RCA agent for dev productivity and nightly issue resolution.
- 3.0.0 Senior Software Engineer — Computer Vision@ Wipro (Client: HSBC Bank) · Gurgaon, India · Oct 2021 → Nov 2022
Built production computer-vision and document-intelligence systems for HSBC's KYC and document-processing pipelines.
- Python
- YOLOv5
- VGG16
- TrOCR
- PaddleOCR
- TF-IDF
### Added
- + High-precision object detection: ID-card localization with YOLOv5 — 95% mAP in production.
- + Multimodal document understanding: Hybrid classifier combining VGG16 visual features with TF-IDF text features — boosted document classification accuracy from 70% → 95%.
- + Inference optimization: Deep-profiled Python pipelines and refactored bottlenecks for a 4× reduction in request latency and significant memory savings.
- + OCR solutions: Deployed Transformer-based OCR (TrOCR, PaddleOCR) for multi-lingual handwritten documents at 90% accuracy.
- 2.0.0 Senior Software Engineer — Computer Vision@ ARTIVATIC.AI · Bangalore, India · Jun 2021 → Oct 2022
Built computer-vision pipelines for insurance — e-KYC, identity verification, predictive risk attributes from facial imagery, and signature fraud detection.
- Python
- OpenCV
- dlib
- Siamese Networks
- ResNet50V2
- YOLOv4
### Added
- + Identity verification pipeline: Complete e-KYC using Siamese Networks for face matching and dlib for liveness detection — critical for insurance fraud prevention.
- + Predictive modelling: Fine-tuned classifiers to predict Age, Gender, BMI, and Smoker Status from facial imagery.
- + Document intelligence: Improved signature fraud detection accuracy from 45% → 81% using a ResNet50V2 backbone for signature matching.
- 1.0.0 Full-Stack Developer@ Snap-on Business Solutions · Noida, India · Jun 2018 → Jun 2021
First professional role. Built scalable web applications and an automated chassis-number detection system using classic image-processing techniques.
- Angular
- Java
- OpenCV
- Agile
### Added
- + Chassis-number detection: Automated system using image processing and OpenCV.
- + Web applications: Scalable apps built with Angular and Java, following Agile methodologies and MVC design patterns.