Frank Goortani
Forward Deployed Engineer • Solutions Engineer • AI Implementation Specialist
Seeking FDE Role at AI Platform Vendor (Anthropic, OpenAI, Databricks, Snowflake, Cohere)
Professional Summary
Forward Deployed Engineer profile: 25+ years shipping production systems, deep LLM expertise, proven ability to embed with customers and deliver solutions independently. Built production AI systems at Uber (award-winning ELLE), implemented AI solutions for customers across industries (legal, fintech, healthcare, retail) via consulting engagements. Technical depth in LangChain, RAG, AI Agents, full-stack development (Python, Go, React) combined with ability to translate customer needs into working technical solutions. 4+ years teaching experience training 100+ engineers on cloud, data warehousing, and web development.
Seeking FDE role at AI platform vendor (Anthropic, OpenAI, Databricks, Snowflake, Cohere) where I can help customers succeed with production AI implementations.
Tools, Frameworks, Libraries, Languages, Databases
AI/ML & LLM Frameworks
LangChain • LangGraph • FastMCP • MCP Development • CrewAI • Ollama • Prompt Engineering • Agentic Workflows • AI Agents • RAG • Multi-Agent Systems • Model Fine-Tuning
Agentic Coding & AI Tools
Claude Code • Cursor • GitHub Copilot • Codex • Aider • AI-Assisted Development • MCP Server Development • 3-5x Productivity Gains
LLM Platforms
OpenAI (GPT-4, GPT-3.5) • Anthropic Claude (Sonnet, Opus) • Google Gemini • Cohere • Meta LLMs • Hugging Face Models • Open Source LLMs • Model Selection • Model Evaluation
Vector Databases & Embeddings
OpenSearch • Pinecone • Qdrant • Chroma • Weaviate • Embedding Models • Semantic Search • Vector Similarity Search • Re-ranking
Backend Frameworks
Python (FastAPI, LangFx, Pydantic, Uvicorn, async/await) • Go (YARPC, gRPC, microservices) • Node.js (Express, NestJS) • Java (Spring Boot) • C# (.NET Core)
Frontend Frameworks
React 18 • TypeScript 5.7 • NextJS • Fusion.js • TanStack React Query • Redux • Angular • VueJs • BaseUI • shadcn/ui • Tailwind CSS • Material Design
Cloud Platforms
Google Cloud (GCP Cloud Run, Firestore, Firebase, GKE) • AWS (EC2, Lambda, S3, RDS, DynamoDB, ECS) • Azure (VMs, Functions, CosmosDB, AKS) • Serverless Architecture
Container & Orchestration
Docker • Kubernetes (K8S) • Helm • Istio • Rancher • Google Kubernetes Engine • Container Orchestration • Service Discovery
API & Integration
GraphQL • gRPC • REST • Protocol Buffers • Apache Thrift • Swagger/OpenAPI • OAuth2/OIDC • JWT • Webhooks • API Gateway
Data & Messaging
Apache Kafka • Cadence • Temporal • Apache Spark (PySpark) • Presto • Hive • HDFS • Airflow • Event-Driven Architecture • Stream Processing
Databases
Cassandra • MySQL • PostgreSQL • MongoDB • Redis • DynamoDB • ElasticSearch • Firestore • CouchDB • SQL Server
DevOps & CI/CD
Bazel • GitHub Actions • Jenkins • Bamboo • Circle CI • Terraform • Ansible • Chef • Infrastructure as Code • GitOps • Continuous Deployment
Monitoring & Observability
M3 • Prometheus • Grafana • Datadog • Splunk • Zap Logging • Tally Metrics • Jaeger (Distributed Tracing) • Kibana • Application Performance Monitoring
Security & Compliance
OAuth2/OIDC • RBAC • Encryption (TLS, mTLS, PKI) • HIPAA Compliance • SOC 2 • GDPR/CCPA • Security Best Practices
Skills and Achievements
Customer Success & Consulting:
- Customer Engagement: Delivered 15+ production AI implementations with 80% client retention and 4.5/5 satisfaction
- Requirements Gathering: Conducted 50+ customer discovery sessions across privacy, security, legal, and business teams
- Solution Design: Translated business problems into technical solutions achieving measurable ROI (60% time savings, $200K+ annual savings)
- Technical Training: Trained 100+ engineers and customers on AI/ML systems, cloud migration, and technical best practices
- Cross-Functional Collaboration: Partnered with engineering, product, legal, and business teams across 8+ industries
- Customer Documentation: Created comprehensive technical documentation, user guides, and training materials for non-technical audiences
- Post-Deployment Support: Provided ongoing support via Slack, office hours, and troubleshooting sessions
AI/ML Implementation:
- Production LLM Systems: Built and deployed RAG systems, AI agents, and multi-agent architectures for real-world customer use cases
- Industry Expertise: Legal tech (document analysis, legal research), fintech (fraud detection, risk analysis), healthcare (diagnostic support, HIPAA compliance), retail (product recommendations, customer analytics)
- Integration Patterns: Integrated LLM systems with existing customer infrastructure (Shopify APIs, HIPAA-compliant architectures, enterprise data pipelines)
- Model Selection: Advised customers on OpenAI vs Anthropic vs Google Gemini vs Cohere based on use case requirements
- Prompt Engineering: Achieved 90%+ accuracy on customer use cases through prompt optimization and few-shot learning
- Vector Databases: Implemented semantic search with OpenSearch, Pinecone, Qdrant for document retrieval
Technical Execution:
- Full-Stack Development: React/TypeScript frontend, Python/FastAPI backend, Go microservices, cloud-native applications
- Rapid Prototyping: Built MVPs in 4-8 weeks from requirements to production for customer engagements
- Cloud Architecture: GCP, AWS, Azure deployments with serverless, containerized, and managed services
- Data Pipelines: Apache Spark, Kafka streaming, real-time analytics for customer data processing
- DevOps & Automation: CI/CD pipelines, infrastructure as code, container orchestration for customer deployments
- Security & Compliance: HIPAA-compliant architectures, GDPR/CCPA compliance, encryption at rest/transit
Communication & Leadership:
- Technical Presentations: Delivered presentations to C-level executives, VPs, and technical teams across customer organizations
- Whiteboarding: Solution design sessions with customers translating complex technical concepts into visual diagrams
- Customer Workshops: Led workshops on AI/ML best practices, prompt engineering, agentic coding with Claude Code, and production LLM systems
- Teaching Experience: 4+ years as corporate and college instructor (Cloud Migration, Data Warehousing, Web Development, BI)
- Thought Leadership: 13K+ LinkedIn followers, published technical articles reaching 50K+ readers
- Cross-Industry Versatility: Legal, fintech, healthcare, retail, manufacturing, hospitality, insurance, banking
Professional Experience
Customer-Facing Consulting:
Delivered 15+ production AI implementations for customers across legal tech, fintech, healthcare, ecommerce, manufacturing, hospitality industries. Embedded with customer teams to understand requirements, design solutions, implement systems, and ensure successful adoption.
Customer Success Metrics:
- 80% client retention (customers return for additional projects)
- 90% would recommend to peers (measured via surveys)
- Average project delivery: 4-8 weeks from requirements to production
- Customer satisfaction: 4.5/5 average rating
- Revenue: $125K+/year in consulting revenue through inbound and referrals
Representative Engagements:
Legal Tech - Document Analysis System
Customer: Mid-sized law firm (50+ attorneys)
Challenge: Manual document review taking 100+ hours/week, needed AI automation
Solution Delivered:
- Built RAG system for legal document search (Pinecone vector DB, 100K+ documents indexed)
- Implemented multi-agent system for automated legal research, document drafting, case analysis
- Designed full-stack web app (React, NextJS, Python FastAPI, Firebase)
- Integrated multiple LLM providers (OpenAI GPT-4, Anthropic Claude) with fallback strategies
- Provided customer training on prompt engineering and AI best practices
Customer Impact:
- 60% reduction in manual document review time
- $200K+ annual savings in paralegal hours
- 3-month ROI on implementation cost
Technologies: Python (LangChain, LangGraph), React, NextJS, Firebase, GCP, OpenAI, Anthropic Claude, Pinecone
Fintech - Fraud Detection & Risk Analysis
Customer: Fintech startup (Series A, payments platform)
Challenge: High false-positive rate in rule-based fraud detection, needed ML solution
Solution Delivered:
- Built AI-powered fraud detection using LangChain and RAG for transaction pattern analysis
- Implemented real-time risk scoring with sub-100ms latency
- Designed alert system with Kafka messaging and dashboard (React, TypeScript)
- Trained customer team on prompt engineering and model fine-tuning
Customer Impact:
- 40% reduction in false positives (improved customer experience)
- 25% increase in fraud detection accuracy
- $500K+ annual savings in fraud losses
Technologies: Python (LangChain, FastAPI), Kafka, React, TypeScript, OpenAI, Qdrant (vector DB)
Healthcare - Patient Data Analysis
Customer: Healthcare AI startup (diagnostic support tools)
Challenge: Needed RAG system for medical literature search and diagnostic recommendations
Solution Delivered:
- Built medical RAG system indexing 50K+ medical journals, research papers, clinical guidelines
- Implemented AI agents for differential diagnosis suggestions based on patient symptoms
- Designed HIPAA-compliant architecture (encryption at rest/transit, audit logging)
- Provided customer training on prompt engineering for medical use cases
Customer Impact:
- 50% faster medical literature search for physicians
- 90% accuracy on differential diagnosis suggestions (validated by medical experts)
- HIPAA compliance certified by third-party auditors
Technologies: Python (LangChain, LangGraph), OpenSearch (vector DB), OpenAI, GCP (Cloud Run, Cloud Functions), HIPAA-compliant infrastructure
Ecommerce - Product Recommendation Engine
Customer: Mid-market ecommerce company ($50M+ revenue)
Challenge: Generic product recommendations, needed personalized AI-driven recommendations
Solution Delivered:
- Built AI recommendation engine using RAG for product catalog search and customer preference analysis
- Implemented real-time personalization based on browsing history, purchase patterns
- Integrated with existing ecommerce platform (Shopify) via APIs
Customer Impact:
- 30% increase in conversion rate
- $2M+ additional revenue in first 6 months
- 20% increase in average order value
Technologies: Python (LangChain), React, Shopify APIs, Pinecone, OpenAI
Customer-Facing Work:
Built ELLE AI Decision Engine serving 500+ internal customers (Privacy, Security, Legal teams globally).
Customer Engagement:
- Conducted 50+ customer discovery sessions with privacy engineers, security analysts, legal reviewers
- Gathered requirements across 6+ use cases (Technical Privacy Review, Security Guidance, Legal Review, etc.)
- Delivered training sessions to 100+ users on using ELLE for automated workflows
- Provided ongoing support via Slack, documentation, and office hours
- Created comprehensive documentation for non-technical users (legal, privacy, compliance teams)
Customer Success:
- 70% reduction in manual review time (customer reported)
- 500+ users across global teams (99% adoption within Privacy org)
- 4.5/5 satisfaction rating (measured via internal surveys)
- Won multiple innovation awards for customer impact and automation efficiency
Technical Implementation:
- Built production LLM system (LangChain, LangGraph, RAG) processing millions of decisions
- Architected multi-agent system with 6+ automated workflows
- Implemented full-stack web app (React, TypeScript, Fusion.js) for user interface
- Integrated with 30+ Uber platform services (USSO, Charter, Cadence, M3, Zap)
Technologies: Python (LangChain, LangGraph), Go (YARPC, gRPC), React, TypeScript, OpenAI, OpenSearch, Kafka, Cadence
Customer-Facing Projects:
- Customer 360 Platform: Worked with marketing teams to understand customer data needs, designed unified customer data platform
- Inventory Management: Embedded with store operations teams to understand pain points, built real-time inventory sync system
- Team Leadership: Led team of 10+ onshore and 50+ offshore developers
Customer Impact:
- $50M+ in targeted marketing revenue through personalized customer insights
- 20% improvement in in-store product availability
- 25% increase in customer satisfaction through personalized recommendations
Technologies: Java Spring Boot, Kafka, Cassandra, Redis, React, Angular, AWS, Kubernetes
Teaching & Training Experience (Customer-Facing Communication)
Corporate Instructor - The Judge Group (2017-2019)
- Taught "Migration to Cloud" course to enterprise organizations
- Covered cloud migration phases: Decision Making, Planning, Architecture, CI/CD, Operations, HA/DR
- Trained 50+ engineers on AWS, Azure, and GCP migration patterns
College Instructor - George Brown College (2014-2017)
- Developed comprehensive course for Web and Business Intelligence using SQL Server 2012
- Taught 40+ students per semester on data warehousing, BI, and web development
- Created hands-on labs and practical assignments for real-world skill development
International Instructor - Learning Tree (2014-2015)
- Provided online and in-class courses across North America (Washington DC, Toronto)
- Course: "Designing an Effective Data Warehouse"
- Trained 30+ students on data modeling, ETL, and BI best practices
College Instructor - Cestar College / Lampton College (2013-2016)
- Developed entry-level to advanced curricula: Ruby on Rails, JavaScript, Java, C#, iOS, Android
- Taught 50+ students full-stack web and mobile development
- Created project-based learning curriculum for hands-on experience
Impact: Trained 100+ students who went on to careers in software engineering, data science, and cloud architecture
Technical Interviews & Customer Success
Conducted 100+ technical interviews for top-tier companies:
- Robinhood, Netflix, Airbnb, Peloton, Gap, and other FAANG/tech companies
- Evaluated candidates on coding skills, system design, and cultural fit
- Provided feedback to hiring managers on candidate strengths and areas for improvement
Customer Success Skills:
- Communication: Technical presentations to C-level executives, whiteboarding sessions, workshops
- Problem Solving: Rapid requirements gathering, translating business problems into technical solutions
- Project Management: End-to-end ownership of customer implementations, stakeholder management
- Post-Deployment Support: Ongoing optimization, troubleshooting, and feature enhancements
Industry Expertise
Legal Tech: Document analysis, legal research, case management, DSAR automation, privacy compliance
Fintech: Fraud detection, risk analysis, payment processing, financial data analysis
Healthcare: Medical literature search, diagnostic support, patient data analysis, HIPAA compliance
Retail/Ecommerce: Product recommendations, customer analytics, inventory management, personalization
Manufacturing: Supply chain optimization, quality control, predictive maintenance
Hospitality: Customer experience optimization, booking systems, personalization
Insurance: Policy management, claims processing, risk assessment
Banking: Payment processing, fraud detection, customer analytics
Education and Professional Development
M.Sc. in Management
AmirKabir University of Technology (Tehran Polytechnic), Iran | 2003
B.Sc. in Computer Software Engineering
AmirKabir University of Technology (Tehran Polytechnic), Iran | 2001
Certifications:
- Certified Data Scientist, Johns Hopkins University, 2015
- PMP Certified (Project Management Professional), PMI, 2013
- TOGAF 9.1 Certified, The Open Group Architecture Framework, 2013
- ITIL Foundation, Mind Leaders, 2013
- MCPD - Web Application Development 2008, Microsoft, 2012
- MCITP - Database Developer 2008, Microsoft, 2012
- Agile Project Management Strategy and Analysis, 2013
- Data Warehousing Management, Mind Leaders, 2013
- OWASP Top 10, Security Compass, 2012
Thought Leadership & Community
LinkedIn: 13K+ followers • AI/ML thought leader
- Published 50+ posts on production LLM systems, customer success with AI, implementation best practices
- Average engagement: 10K+ views per post, 200+ likes
Medium: @FrankGoortani
- Published articles on LangChain, RAG systems, AI implementation, customer success patterns
- Total views: 50K+, growing 20%/month
Speaking & Publications:
- Available for conference talks on production AI implementation, customer success with AI, FDE best practices
- Guest on AI podcasts discussing customer-facing AI work and real-world challenges
Why I Excel as a Forward Deployed Engineer
- Customer Empathy: 15+ successful customer engagements, 80% retention, 4.5/5 satisfaction
- Technical Depth: Deep LLM expertise (LangChain, RAG, AI Agents) from production implementations
- Full-Stack Capability: Can build end-to-end solutions (React frontend, Python backend, cloud infrastructure)
- Industry Versatility: Experience across 8+ industries (legal, fintech, healthcare, retail, manufacturing, hospitality, insurance, banking)
- Independent Execution: Proven ability to own customer engagements end-to-end with minimal supervision
- Communication Excellence: 4+ years teaching experience, 100+ students trained, 100+ technical interviews conducted
- Results-Oriented: Focus on customer outcomes (ROI, time savings, revenue impact) with measurable results
- Cross-Functional Collaboration: Partnered with engineering, product, legal, business teams across organizations
Target Companies & Roles
Preferred: Anthropic, OpenAI, Databricks, Snowflake, Cohere, LangChain, Pinecone, Qdrant, Weaviate, Hugging Face
Role: Forward Deployed Engineer, Solutions Engineer, Customer Success Engineer (Technical), Field Engineer
Compensation: $200K-$300K (base + equity)
Work Style: Remote-only, customer-facing, autonomous execution, cross-industry engagements