Frank Goortani
Solution Architect | AI/ML Engineer | Full-Stack Developer
Professional Summary
Senior AI/ML Engineer and Solution Architect with 25+ years of full-stack development experience, specializing in production LLM systems, distributed microservices, and enterprise-scale web applications. Currently Full-Time CTO at FasterOutcomes, scaling an AI legal tech startup from early revenue to Series A. Previously Solution Architect at Uber, where I built the award-winning ELLE AI Decision Engine (won multiple internal innovation awards) - an LLM-powered security and privacy automation platform processing millions of decisions across Uber's global operations.
Deep expertise in LangChain, LangGraph, RAG systems, and AI Agents with proven track record shipping production AI/ML features at scale. At Uber, led development of 9+ enterprise privacy web applications using React 18, TypeScript, and Fusion.js, and contributed to 50+ security infrastructure projects in Python and Go. Architected authorization middleware achieving 96% test coverage while solving complex distributed systems challenges.
At FasterOutcomes, leading product strategy and technical architecture for production LLM applications, scaling engineering team from 3 to 15+ engineers. Active thought leader with 13K+ LinkedIn followers and technical blog on Medium. Proven ability to work across the full stack (backend, frontend, AI/ML, cloud, security) while maintaining exceptional code quality and system reliability.
Core Specialties: Production LLM Systems • AI Agents • RAG • LangChain/LangGraph • Go Microservices • React/TypeScript • Python (FastAPI/LangFx) • Distributed Systems • Cloud Architecture (GCP/AWS) • Security Engineering
Core Technical Skills
AI/ML & LLMs
• LangChain, LangGraph
• FastMCP, MCP Development
• AI Agents, RAG Systems
• OpenAI, Anthropic Claude
• Google Gemini, Cohere
• PyTorch, TensorFlow
• Prompt Engineering
Agentic Coding & AI Tools
• Claude Code, Cursor
• GitHub Copilot, Codex
• Aider, AI-Assisted Dev
• MCP Server Development
• Agentic Workflows
• 3-5x Productivity Gains
Backend Development
• Go (YARPC, gRPC)
• Python (FastAPI, Pydantic)
• Java (Spring Boot)
• Node.js, TypeScript
• Microservices, REST APIs
• Cadence, Temporal
• Apache Kafka
Frontend Development
• React 18, Hooks
• TypeScript 5.7+
• Fusion.js, NextJS
• TanStack React Query
• BaseUI, shadcn/ui
• Styletron, TailwindCSS
• Jest, Playwright
Cloud & DevOps
• Google Cloud (GCP)
• AWS, Azure
• Docker, Kubernetes
• Bazel, Helm
• GitHub Actions, Jenkins
• Terraform, IaC
• M3, Prometheus
Databases & Data
• Cassandra, MySQL
• PostgreSQL, MongoDB
• Redis, DynamoDB
• OpenSearch, Pinecone
• Qdrant, Chroma
• Apache Spark, Presto
• Kafka Streams
Security & Identity
• Charter (Uber IAM)
• SPIFFE/SPIRE
• OAuth2/OIDC, JWT
• RBAC, authfx
• GDPR/CCPA
• KMS, PKI
• Security Engineering
Professional Experience
Leading AI/ML and full-stack development initiatives across Uber's Technical Privacy and Engineering Security organizations. Built award-winning production LLM systems, architected enterprise web applications, and contributed to 50+ security infrastructure projects.
ELLE (EngsecLLM Engine) - AI Decision Engine - Primary Project
Award-winning LLM-powered security and privacy automation platform
Achievement: Won multiple internal Uber innovation awards for automating Technical Privacy review workflows, reducing manual review time by 70% while improving decision consistency.
Technical Leadership:
- Architected and built production LLM system processing millions of security/privacy decisions across Uber's global platform
- Designed multi-agent architecture using LangChain and LangGraph for self-correcting AI workflows:
- Processor Agent + Reviewer Agent pattern with iterative improvement
- RAG (Retrieval Augmented Generation) with OpenSearch vector store integration
- Template-based prompt management for consistent decision-making
- Structured output parsing with Pydantic for reliable data extraction
- Implemented 6+ automated workflows: Technical Privacy Review (L1/L2 data classification gates), Security Guidance automation, Legal Review routing, Privacy Impact Assessment, Community Operations Review, JIRA prioritization (automated P0/P1/P2 assignment based on risk analysis)
- Built Google Docs ingestion pipeline for document RAG with semantic search and metadata filtering
- FastAPI service architecture with /agent and /agent-for-template endpoints
- Integrated with Uber platform: M3 metrics, Flipr feature flags, JIRA automation, Salesforce
- Designed authorization middleware (Go, YARPC, Charter) with 96% test coverage:
- Dual middleware architecture (inbound/outbound) for comprehensive endpoint coverage
- Eliminated circular dependencies using MCP Gateway pattern with fx.Lifecycle hooks
- LDAP group-based access control with Charter Decision Service integration
- SPIFFE ID extraction and employee ID mapping
Technologies: Python (FastAPI, LangChain, LangGraph, FastMCP, Pydantic), Go (YARPC, gRPC, Protocol Buffers), OpenAI API, Anthropic Claude, OpenSearch (vector store), Cadence workflows, Cassandra, Redis, M3 metrics, Bazel, Claude Code (agentic development)
Business Impact:
- Automated 70% of privacy review decisions, reducing review time from days to minutes
- Enabled 6+ security use cases with consistent LLM-powered decision-making
- Centralized GenAI platform serving multiple Engineering Security teams
- Charter audit trail for compliance and security governance
- Achieved 3-5x development velocity leveraging Claude Code and agentic coding workflows
Enterprise Privacy Web Applications (9 Production Applications)
Full-stack development using React 18, TypeScript, Fusion.js
Led development of 9+ enterprise privacy web applications for Uber's Technical Privacy team, serving thousands of internal users with production-grade security, comprehensive testing, and Uber platform integration.
Applications:
- Elle Web - EngsecLLM Engine Frontend (LLM-powered privacy review automation UI, ERD submission workflows, real-time decision flow, JIRA integration)
- IRIS Web - Privacy Web Frontend (Privacy request management, multi-region GDPR/CCPA compliance, GeoIP integration, Presidio WebView)
- DSAR Web - Subject Access Request Automation (GraphQL-based data fetching, schema stitching, Privacy Guard Challenger integration)
- Consents Manager Web - Contextual consent management (Consent resource creation, management key generation, RPC-based microservice architecture)
- Web CMP - Consent Management Platform (IAB-approved TCF v2 implementation, cross-border consent management, Rosetta internationalization)
- TPRM QC Copilot Web - Third-Party Risk Management (LLM-powered quality control for vendor risk assessment, CSV data parsing)
- Agentic AI Observability Web - LLM observability and monitoring platform
- Design Review Web - Privacy design review workflow automation
- Privacy Playground - Privacy engineering experimentation platform
Cross-Application Technical Excellence:
- Comprehensive testing: Jest (34+ test suites), Playwright (E2E), React Testing Library
- Type-safe RPC: Protocol Buffers with automated TypeScript code generation
- Universal rendering: Fusion.js SSR with client hydration, code splitting, performance optimization
- Platform integration: 30+ Uber services (USSO auth, Flipr feature flags, M3 metrics)
- Security: CSRF protection, CSP, JWT sessions, secure headers, production-grade security
- Observability: M3 metrics, structured logging, distributed tracing, APM
- Build system: Bazel monorepo, Yarn Workspaces, TypeScript project references
- Design system: BaseUI compliance, Styletron CSS-in-JS, responsive design, accessibility (ARIA, keyboard navigation)
Security Infrastructure Projects (50+ Python Projects)
Enterprise-scale Python monorepo across AI/ML, data engineering, and security
AI-Powered Security (AI CoE - Center of Excellence):
- SecureCode AI: Privacy-aware static code analysis for PII detection in logs (LLM-based code scanning, VSCode extension, regex + LLM hybrid detection)
- Agent Guardrails: Security controls for AI agents (permission checking, reflection-based guardrails, MCP integration)
- Custom MCP Servers: Built Model Context Protocol servers for agentic workflows and tool integrations (FastMCP, Python)
- Container Image Remediation Assistant: LangFx Service with LangGraph (AI-powered vulnerability remediation, JIRA integration)
- Agentic Coding Infrastructure: Claude Code, Cursor, and GitHub Copilot integration for team development acceleration
LLM Security Engine (engsec_llm_engine):
- Self-correcting multi-agent system (Processor + Reviewer agents)
- RAG with OpenSearch vector store, Google Docs ingestion
- Jira prioritization automation (L1/L2 gates, risk analysis, P0/P1/P2 assignment)
- FastAPI service with M3 metrics, Flipr feature flags
Data Security & Privacy:
- DataK9: ML-based data classification and protection
- Privacy Export: DSAR automation with LangFx service
- Privacy Guard: Real-time privacy monitoring
- GDPR Compliance Automation: Regulatory compliance tools
Data Engineering Pipelines:
- Apache Spark (PySpark), Kafka streaming, Presto SQL
- ETL/ELT pipelines, incremental processing, backfill automation
- Piper workflow orchestration, YARN resource management
Technologies: Python 3.9/3.11, LangChain, LangGraph, FastMCP, PyTorch, TensorFlow, Apache Spark, Kafka, Presto, Pandas, OpenSearch, Pinecone, Qdrant, FastAPI, Protocol Buffers, Bazel, pytest
AI-powered legal technology startup automating legal workflows with production LLM systems. Started part-time, built MVP in 12 weeks, went full-time January 2026 to scale to Series A.
Leading technical strategy, product architecture, and engineering execution for AI legal tech platform serving law firms and corporate legal departments. Built production LLM applications from 0→1 with focus on RAG systems, AI agents, and agentic workflows.
Technical Leadership & Product Strategy:
- Architected complete AI platform from ground up: Production RAG system for legal document analysis, AI agents for contract review, agentic workflows for multi-step legal processes, vector database integration (Pinecone)
- Led product roadmap and go-to-market strategy: Identified product-market fit, designed user workflows, prioritized features
- Built MVP in 12 weeks with small team: 0 to production-ready platform with real customers and revenue
- Technology stack decisions: Backend (Python, FastAPI, LangChain, LangGraph, Firebase Functions), Frontend (React, NextJS, TypeScript, TailwindCSS), Cloud (GCP - Cloud Run, Firestore, GCP Storage), AI (OpenAI GPT-4, Anthropic Claude, Cohere)
Business & Team:
- Hands-on technical work (70%) + strategic leadership (30%)
- Hiring and mentoring engineering team
- Stakeholder management (investors, customers, legal advisors)
Key Achievements:
- Shipped production LLM features serving real law firms
- Achieved product-market fit in legal AI automation space
- Built scalable AI platform with modern cloud-native architecture
- Maintained security and privacy compliance for sensitive legal data
Technologies: Python (FastAPI, LangChain, LangGraph), React, NextJS, TypeScript, Firebase/Firestore, GCP (Cloud Run, Cloud Functions), OpenAI, Anthropic Claude, Pinecone, TailwindCSS, shadcn/ui
GenAI consulting and technical recruiting for top tech companies
GenAI Lead for Client Projects:
- Led AI/ML implementations across industries: ecommerce, manufacturing, accounting, legal, hospitality
- Architected production LLM solutions (RAG systems, AI agents, chatbots)
- Delivered custom AI automation for client workflows
- Full-stack implementations (Python backend, React frontend, cloud deployment)
Technical Interviewing (100+ Interviews Conducted):
- Conducted technical interviews for top tech companies: Uber, Robinhood, Netflix, Airbnb, Peloton, Gap, and others
- Evaluated candidates for senior engineering roles (Staff, Principal, Senior)
- Interview domains: Backend, Frontend, System Design, AI/ML, Security
- Built deep network across tech industry through interview work
Notable Projects: Counta AI (agentic accounting automation), Lucid AI Solutions (ecommerce AI), Manufacturing AI (predictive maintenance), Legal Tech AI (document analysis)
Technologies: LangChain, LangGraph, Python, React, NextJS, Firebase, GCP, OpenAI, Anthropic, various client-specific stacks
Led enterprise architecture and development for Home Depot Canada's digital transformation initiatives, focusing on ecommerce platform, mobile applications, and omnichannel customer experience.
Key Responsibilities:
- Led mobile app development (iOS/Android) for Home Depot Canada (Native iOS - Swift, SwiftUI; Android - Kotlin, Java; 1M+ downloads, 4.5+ star rating)
- Architected ecommerce platform on SAP Hybris (Microservices architecture with Spring Boot, integration with legacy systems)
- Led team of 10+ engineers across mobile, web, and backend
- Technology stack modernization: Java/Spring Boot, Angular, React, cloud migration
Key Achievements:
- Launched mobile app serving 1M+ Canadian customers
- Architected omnichannel platform (web, mobile, in-store integration)
- Led cloud migration from on-premise to hybrid cloud
- Reduced page load times by 40% through architecture optimization
Technologies: Java (Spring Boot), SAP Hybris, iOS (Swift), Android (Kotlin), Angular, React, Microservices, REST APIs, Azure, CI/CD
Led enterprise application development and architecture for TD Bank's internal systems, managing large-scale .NET applications and data platforms.
Key Achievements:
- Delivered enterprise-scale banking applications serving thousands of employees
- Led successful migration from legacy systems to modern .NET stack
- Implemented data warehouse for business intelligence and reporting
- Achieved 99.9% uptime for critical banking applications
Technologies: .NET (C#, ASP.NET), SQL Server, SSIS/SSRS/SSAS, Data Warehousing, Enterprise Architecture
Key Achievements:
- Designed enterprise architecture framework using TOGAF 9.1
- Led development of reusable component libraries
- Architected multi-tenant insurance platform
Technologies: .NET, Angular, TOGAF, Enterprise Architecture, Microservices
Key Responsibilities:
- Full-stack .NET development for CAA membership systems
- SAP Hybris ecommerce implementation
- Integration with legacy systems
Technologies: .NET (C#, ASP.NET), SAP Hybris, SQL Server, Web Services
Key Achievements & Awards
Uber:
- Multiple Internal Innovation Awards - ELLE AI Decision Engine (EngsecLLM Engine)
- Engineering Excellence Award - 96% test coverage on authorization middleware
- Automated 70% of privacy reviews - Reduced review time from days to minutes
- Built 9 enterprise web applications - Serving thousands of internal users
- 50+ security infrastructure projects - Across Python monorepo
- IAB TCF v2 Certification - Web CMP consent management platform
Technical Achievements:
- 96% code coverage on production authorization middleware (exceeding 85% standard)
- 13,000+ LinkedIn followers - Active thought leader in AI/ML and agentic coding
- Published technical articles on Medium (@FrankGoortani)
- 100+ technical interviews conducted for top tech companies (Uber, Netflix, Airbnb, Robinhood)
- Open source contributions - GitHub repositories with AI/ML tools and examples
Leadership & Impact:
- Led teams of 10+ engineers at Home Depot and TD Bank
- CTO - FasterOutcomes (AI legal tech startup, 0→1→Series A)
- Built products from 0→1 - MVP to production in 12 weeks
- Shipped production AI systems - LangChain, RAG, AI Agents at scale
Education
Master of Science (M.Sc.) - Management
AmirKabir University of Technology (Tehran Polytechnic), Tehran, Iran
Graduated: 2003
Bachelor of Science (B.Sc.) - Computer Software Engineering
AmirKabir University of Technology (Tehran Polytechnic), Tehran, Iran
Graduated: 2001
Professional Certifications
- TOGAF 9.1 Certified - The Open Group Architecture Framework (2013)
- PMP (Project Management Professional) - Project Management Institute (2013)
- Certified Data Scientist - Johns Hopkins University (2015)
- ITIL Foundation - IT Infrastructure Library (2013)
- Microsoft MCPD - Web Application Development (2012)
- Microsoft MCITP - Database Developer (2012)
Teaching & Speaking
College Instructor:
- George Brown College - Web Development and Programming
- Cestar College - Software Engineering
- Lampton College - Computer Science
Corporate Training:
- Learning Tree International - Technical instructor
- The Judge Group - Enterprise software training
Publications & Thought Leadership
Medium Blog: medium.com/@FrankGoortani
Technical articles on AI/ML, production LLM systems, and software architecture. Topics: RAG systems, LangChain/LangGraph, AI agents, Claude Code, agentic coding workflows, MCP development, distributed systems
LinkedIn Thought Leadership:
13,000+ followers with regular posts on agentic coding, Claude Code, MCP development, AI/ML engineering, production LLM systems. Active engagement with AI engineering community.
Languages
- English: Fluent (Professional Working Proficiency)
- Farsi (Persian): Native
Additional Information
Current Status:
- Full-Time CTO at FasterOutcomes (AI legal tech startup, January 2026-Present)
- Scaling company from early revenue toward Series A
- Leading engineering team growth and technical strategy
- Not currently seeking new opportunities
Work Preferences:
- 100% remote (non-negotiable for health reasons: back/shoulder issues)
- Async-first culture with minimal meetings
- Results-oriented, high-trust environment
- Modern tech stack (AI/ML, cloud-native, microservices)
Business Entities:
- VisionzLab - AI-driven architecture and development studio
- Architect.solutions - AI-enabled development consulting
- Focus: Agentic coding, Claude Code expertise, MCP development, production LLM systems