Nakshatra Nahar

At a leading AI research organization, I designed and deployed a production supervisor agent architecture for precision agriculture using LangGraph on AWS Bedrock, serving over 50,000 farm units globally. Specialist agents processed IoT sensor streams, satellite NDVI imagery, and real-time weather APIs in parallel. In contrast, a coordinator agent synthesized outputs and generated multilingual crop advisories in eight regional languages using GPT-4o.

I integrated Azure Data Manager for Agriculture as a unified data backbone, eliminating the need for custom ETL pipelines. I implemented the MCP tool servers, connecting agents autonomously to USDA and FAO datasets without human intervention. I also engineered an intent router that classifies crop health, disease risk, yield forecast, and supply chain queries, with zero misrouting across 50,000 daily farm interactions.

As a result, I reduced crop decision latency from 72 hours to four hours with 80% of decisions fully automated and human-in-the-loop escalation for high-risk disease outbreak alerts.

Working for a global AI acceleration firm, I built a centralized AI SDK that serves as the backbone for agricultural product recommendations across a seed and crop protection enterprise with operations in 90 countries and 56,000 employees.

I engineered three core primitives: an intent router that classifies farmer queries into seed selection, crop protection, and agronomic advice domains; a session context store that maintains farm-specific multi-turn conversational context with strict data isolation between regional business units; and a system prompt governance layer that ensures consistent and compliant AI behavior across all applications. I also built reusable Python SDK connectors for Databricks, Snowflake, and SAP integrations, enabling internal engineering squads to ship new AI features from months to weeks. More than 15,000 agronomists used powered agents across six global business functions at enterprise scale.

As an active open-source contributor to OpenAI Codex and the surrounding LLM developer tooling ecosystem, I contributed to production-grade improvements to code generation, prompt evaluation, and agentic workflow infrastructure used by millions of developers globally. I contributed to core prompt engineering frameworks, improving code synthesis accuracy across Python, JavaScript, and TypeScript. I built evaluation harnesses for benchmarking Codex model outputs against real-world developer intent - measuring functional correctness, hallucination rates, and context window utilization across multi-file repository contexts.

I also developed MCP-compatible tool server integrations enabling Codex-powered agents to autonomously interact with external development environments, package registries, and CI/CD systems. Finally, I contributed async Python pipeline improvements for streaming code completions at scale, reducing p99 latency by 34% across high-throughput developer tool integrations. My work was directly referenced in internal OpenAI tooling used across enterprise API customers.

Contracted through an AI research organization to a tier-1 investment bank, I designed and deployed a real-time trading intelligence platform processing 50 million market events per day across global equity, FX, and commodity markets. I built a multi-agent risk assessment system using LangGraph on AWS Bedrock, where specialist agents monitored position limits, market microstructure signals, regulatory exposure thresholds, and counterparty risk simultaneously, generating autonomous risk alerts within 200 milliseconds per event.

I implemented a RAG system over 15 years of proprietary trade data, regulatory filings, and market research using AWS Bedrock Knowledge Bases with semantic chunking and query rewriting. I engineered an intent router that classifies signals into alpha generation, risk management, and compliance domains and dispatches them to the appropriate specialist agents. I built a session context store to maintain live portfolio state across complex multi-turn trader interactions. The solution reduced risk alert latency from 45 seconds to 200 milliseconds, processing 50 million events daily.

Through an AI research and tooling organization, I architected and deployed a production Zero Trust security intelligence platform for a Fortune 100 enterprise managing 240,000 employee identities across 45 countries. I built a multi-agent identity threat detection system using LangGraph on AWS Bedrock, where specialist agents monitored behavioral biometrics, privilege escalation patterns, lateral movement signals, and 3rd-party vendor access simultaneously, generating risk scores and autonomous access revocation decisions within three seconds of anomaly detection.

I implemented MCP tool servers that connect agents to Active Directory, Okta, CyberArk PAM, and SIEM platforms, enabling autonomous policy enforcement for 87% of identity incidents. I also built a RAG pipeline over 50,000+ security policies, SOC2 and ISO27001 compliance frameworks, and historical breach investigation reports on AWS Bedrock Knowledge Bases.

The solution reduced identity-based breach dwell time from 21 days to 47 minutes, saving $18 million annually in breach response costs.

At a leading AI research organization, I built a production AI-native developer experience platform serving four million developers across 180 countries and processing 800 million API calls per month. I designed a multi-agent code intelligence system using LangGraph on AWS Bedrock, where specialist agents handled code review, security vulnerability scanning, dependency analysis, and documentation generation simultaneously, returning enriched developer feedback within 400 milliseconds at peak load.

I implemented a session context store, maintaining individual developer repository context and coding patterns across multi-session interactions with strict tenant isolation per organization. I built a RAG pipeline over two billion lines of indexed open-source code, API documentation, and security advisories using AWS Bedrock Knowledge Bases with hybrid retrieval. Finally, I engineered an intent router that classifies developer queries across code generation, debugging, refactoring, and security remediation domains, achieving a 99.99% uptime SLA and processing 800 million monthly API calls at a global scale.

At an enterprise AI agentic framework company, I designed and deployed a production revenue intelligence platform integrated with enterprise CRM systems, serving 2,000+ sales teams across 60 countries and processing 15 million customer interactions monthly. I built a multi-agent sales intelligence system using LangGraph on AWS Bedrock, where specialist agents analyzed deal signals, competitor mentions, stakeholder sentiment, email thread context, and CRM activity patterns simultaneously, generating autonomous next-best-action recommendations within two seconds per deal.

I implemented the MCP tool servers, connecting agents to Salesforce, HubSpot, LinkedIn Sales Navigator, and email APIs, enabling autonomous CRM enrichment without manual data entry. I built a RAG pipeline over 500,000+ sales playbooks, win-loss analyses, product documentation, and competitive intelligence reports. I engineered a session context store that maintains full deal history and buyer relationship context across complex enterprise sales cycles averaging nine months.

The solution increased the average deal close rate by 28% and reduced the sales cycle length by 34% across all enterprise accounts.

Delivered through a global AI acceleration firm, I architected and deployed a production workforce intelligence platform that processes 50 million employee data points monthly across 3,000+ enterprise clients, managing eight million employees globally. I built a multi-agent HR intelligence system using LangGraph on AWS Bedrock, where specialist agents simultaneously analyzed performance signals, attrition risk indicators, skill gap patterns, compensation benchmarks, and engagement survey responses, generating autonomous workforce insights and intervention recommendations within 60 seconds of data ingestion. I implemented the MCP tool servers, connecting agents to Workday, SAP SuccessFactors, Microsoft Teams, and HRIS platforms, enabling autonomous talent intelligence without manual reporting cycles.

I built a RAG pipeline spanning 10+ million anonymized career trajectory records, industry compensation databases, and organizational psychology research, using AWS Bedrock Knowledge Bases. I achieved 89% accuracy in 90-day attrition prediction, validated against ground truth across 12 enterprise clients, reducing involuntary attrition by 31%.

Contracted through a data and Python engineering firm, I designed and deployed an autonomous customer support intelligence platform for a top-5 global SaaS company processing 120 million support tickets annually across 40 product lines in 25 languages. I built a multi-agent support system using LangGraph on AWS Bedrock, where specialist agents handled ticket classification, knowledge base retrieval, product log analysis, billing query resolution, and escalation routing simultaneously, resolving 73% of tier-1 tickets fully autonomously without human agent involvement.

I implemented a session context store that maintains the complete customer interaction history and product usage context across multi-channel support journeys spanning email, chat, and phone. I built a RAG pipeline over 800,000+ knowledge base articles, product documentation, historical ticket resolutions, and release notes using AWS Bedrock Knowledge Bases with hybrid retrieval. I also engineered an intent router that classifies support intents across 140 product-specific categories, achieving 96% classification accuracy.

The solution reduced average handle time from 18 minutes to three minutes, saving $22 million annually in support operations costs.

Deployed and operated a 24/7 live video streaming platform serving thousands of concurrent viewers with zero downtime tolerance. Made the complete pipeline using MediaMTX for RTMP ingest, FFmpeg for multi-rendition HLS transcoding, and Nginx for high-concurrency segment delivery. Configured MediaMTX authentication hooks, tuned HLS segment durations and buffer sizes, and managed stream lifecycles via the MediaMTX API. Hardened the Linux server by tuning file descriptor limits, TCP stack parameters, including net.core.somaxconn and tcp max syn backlog, and sysctl network buffers to handle sudden connection bursts. Implemented Cloudflare CDN with optimized cache control headers for HLS segments so edge nodes absorbed viewer traffic during high-traffic weather events without hitting the origin. Built automated stream health monitoring with self-healing reconnection and watchdog processes restarting FFmpeg pipelines on crash. Diagnosed and resolved simultaneous file descriptor exhaustion and connection queue overflow, presenting as random timeouts under load. Used netstat, ss, htop, iostat, and sar for real-time bottleneck diagnosis during live traffic events. Achieved 99.97% stream uptime across all sources with zero overnight manual.

• Built and maintained a production Shopify Plus storefront for premium consumer goods, serving 500,000 MAU.
• Developed the front end using Next.js 15 App Router with Server Components, Suspense streaming, server actions, and fine-grained cache revalidation triggered by Sanity and commerce webhooks.
• Built Tailwind CSS components covering product pages, bundle suggestions, checkout UI, and user portal flows, including order history and address management.
• Integrated Sanity CMS using GROQ queries, Studio schema definitions, sanity image URL pipeline, and webhook-driven revalidation.
• Built cart state management, Stripe payment processing with webhook handlers for payments, refunds, and disputes, and tax and shipping calculation flows.
• Migrated search from Algolia to self-hosted MeiliSearch and CMS from Sanity to self-hosted Payload CMS. Added OTP email auth to the Medusa back end with storefront UI flows.
• Deployed to a self-managed Ubuntu Linux server using NGINX reverse proxy, OpenResty for custom Lua routing, Docker Compose, and PM2 cluster mode with zero-downtime GitHub Actions deployments.
• Instrumented with Sentry and Lighthouse CI, gating deployments on CWV. Achieved sub-1.2-second LCP and 99.8% uptime.

• Built a consumer local discovery directory platform end to end using Next.js 15 App Router on Vercel with TypeScript.
• Shipped directory with instant faceted search, filter relaxation, fallback queries, Mapbox map view, schema.org JSON-LD on every listing, dynamic OG images via Vercel OG, PWA manifest, sitemap, and PostHog analytics.
• Built operator monetization with magic-link auth via Clerk, Stripe Checkout with tiered pricing, Stripe Tax, webhook handlers with signature verification, dunning sequences, anti-fraud rate limiting, and CRM event triggers for abandoned claims.
• Built operator dashboard with onboarding, field-level edit controls separating operator-owned from pipeline-owned data, CDN-backed photo uploads with validation, and embedded Stripe customer portal.
• Built bidirectional Postgres sync between external source-of-truth and Neon PostgreSQL with webhook subscriptions, retry logic, dead-letter queue, nightly reconciliation, and field-level conflict resolution.
• Built an admin panel with claim approval, DSAR handler, right-to-erasure, and data export. Configured Cloudflare Pro WAF and bot management.
• Achieved Lighthouse 90-plus mobile and Core Web Vitals Good thresholds. Full GDPR, CCPA, and WCAG 2.1.

• Translated a 47-tab Excel fund-level financial planning model into a sustainable Python production architecture while maintaining Excel as the living source of truth throughout.
• Built a reconciliation harness that ran both systems against identical inputs and flagged numeric discrepancies above a one-dollar tolerance on every code change in CI.
• Implemented fund-level modeling logic in pure Python, including investor waterfall distributions across multiple capital stack tiers, preferred return calculations, catch-up provisions, IRR-threshold promote structures, multi-property cash flow projections, liquidity planning, and capital resource planning across varying acquisition and exit dates.
• Architected a four-layer system: Pydantic data ingestion with schema validation, pure functional calculation layer with independent unit tests, openpyxl output layer generating Excel-formatted reports for CEO comparison, and FastAPI service exposing the calculation engine for downstream dashboard consumption.
• Used Claude to accelerate VBA-to-Python translation by explaining business logic before generating code, reducing translation time by 60% while improving documentation quality.