Abhishake
Gupta

• Embedded technical lead across 35+ enterprise clients — scoped per-client requirements, coordinated 10+ engineers on features and integrations, and owned incident response end-to-end on a multi-tenant platform.
• Established AI-native engineering standards adopted org-wide: built do-ai-context — a shared Claude Code context layer with domain files, PII/secrets safety hooks, and LLM-orchestrated release workflows — cutting triage costs ~$4,600/yr.
• Built PageIndex, a codebase navigation index reducing AI triage steps by ~75%; automated release workflow cross-referencing Asana, GitHub, and cherry-pick history via LLM orchestration.
• Built Slack-triggered AI debug agent (Claude SDK + Celery) with brand-scope isolation and PII guardrails.
• Drove 99% reduction in menu sync latency (40m → 3s) via distributed Redis caching across 35+ restaurant brands.
• Led multi-provider delivery orchestration (DoorDash, Uber Eats, Relay) with unified abstraction layer for quoting, dispatching, and tracking; designed real-time failover mechanism reducing order cancellations.
• Hardened inbound webhooks with HMAC signature verification, timestamp checks, replay protection; owned OAuth2/JWT auth for internal and external APIs.
• Owned security hardening — reduced audit cycles from 30+ hrs to 6 hrs via AI-assisted tooling; shipped admin 2FA, per-brand password rotation, login audit trail, and breach incident response.
• Led Flask 1.x → Flask 3 + Connexion 3 (ASGI/uvicorn) migration and shipped ML-powered recommendations v2.

Stack: Python Flask · Redis · React · Docker · JWT · Datadog · Claude SDK · NMI · FreedomPay · Celery

• Built a Python-based Aviation Resource Optimization System — leveraged heuristic algorithms to model complex operational and regulatory constraints, generating compliant, cost-efficient flight and crew plans.
• Built data pipelines (Python, Pandas, Redis) to ingest schedule, crew, and resource data; optimized outputs reduced reliance on third-party vendors and lowered fuel and operational costs.
• Built Golang/Fiber microservices to manage hierarchical data structures and CRUD workflows.
• Worked API-first: kept OpenAPI/Swagger docs in sync and used Postman collections for contract-driven testing.
• Containerized components with Docker and deployed on Linux-based environments; collaborated in a 4-member cross-functional team.

• Enhanced a Flask-based platform for enterprise storage hardware data collection and analysis across multiple vendors.
• Reduced API latency by 60% via RabbitMQ and Celery automation.
• Designed data models and access controls with clear tenant boundaries for customer-facing workflows across multiple accounts.
• Migrated monolithic application to Kubernetes-based microservice architecture; refactored frontend to React SPA.
• Developed internal CLI tooling to reduce support SLAs by streamlining common operational workflows.

• Designed a customer support/ticketing history microservice using Node.js, Express, and Kafka (event-driven ingestion, auditability, and replay-safe processing).
• Developed Django REST APIs for healthcare analytics (descriptive and predictive analysis of patient vitals).
• Built internal CLI tools for FTP/SFTP access scheduled via Autosys.
• Automated GoldenGate replication delay calculation and reporting from Sybase to Oracle.

• Partnered with developers to implement a monitoring/observability tool API in Django for faster incident triage.
• Integrated with Zabbix for automated alerts and issue tracking.
• Reduced manpower required for issue identification and escalation by 80%.

• Developed Flask-RESTful backend APIs for an automated ticketing proof-of-concept serving a mobile frontend.
• Provided L2 support for server applications using Splunk, ELK, and Oracle Siebel CRM.
• Collaborated with cross-functional teams on automated ticketing system implementation.