# Dylan Goldblatt - AI Automation & LLMOps Specialist

> AI Tactics, Strategy, and Logistics - From Problem to Pilot

## Identity & Contact

- **Name**: Dylan Goldblatt
- **Role**: AI Strategy & Research Admin at Kennesaw State University
- **Location**: Atlanta, GA
- **Website**: [Portfolio Site]
- **Email**: Available via booking widget
- **Newsletter**: "Bits of Brilliance" at bitsofbrilliance.xyz
- **GitHub**: @ngoldbla
- **HuggingFace**: @ndgold  
- **LinkedIn**: @ndgold

## Core Expertise & Services

### Primary Focus Areas
- **AI-driven automation**: Clearing space for human judgment through strategic automation
- **Business process optimization**: Friction audits that map cognitive load and context loss
- **LLMOps mastery**: Production-ready LLM agent deployment and orchestration
- **Frontier tech integration**: Bleeding-edge AI capabilities in practical applications

### Methodology: "From Problem to Pilot"
- **Friction audits**: Mapping where cognitive load accumulates and context is lost
- **Living memory systems**: Automation with persistent context and learning capability
- **Shared context layers**: Vector stores, event logs, knowledge graphs for agent memory
- **Single engagement delivery**: Scope, prototype, validate, ship within one funding cycle

### Key Value Propositions
- **No handoffs**: Context layer travels with prototype from idea to deployment
- **Rapid iteration**: Academic rigor with industry execution speed
- **Memory-driven improvement**: Systems that recall history and refine processes
- **Public impact focus**: Building for collective context growth, not just internal efficiency

## Technical Proficiencies

### Programming & Development (Expert Level)
- **Python**: Expert - Primary language for AI/ML development
- **JavaScript/TypeScript**: Expert - Full-stack web development
- **React**: Expert - Modern frontend applications
- **FastAPI**: Advanced - API development and microservices
- **Tailwind CSS**: Expert - Responsive design systems

### AI & Machine Learning
- **OpenAI API**: Expert - GPT integration and fine-tuning
- **Agent Orchestration**: Expert - Multi-agent systems and workflows
- **RAG Systems**: Expert - Retrieval-augmented generation architectures
- **Foundation Models**: Advanced - Working with latest LLMs and multimodal models
- **TensorFlow/PyTorch**: Advanced - Custom model development
- **Vector Databases**: Intermediate - Embedding storage and retrieval

### Data & Analytics
- **Pandas/NumPy**: Expert - Data manipulation and analysis
- **Supabase**: Advanced - Backend-as-a-service integration
- **Palantir Foundry**: Intermediate - Enterprise data platform
- **Palantir AIP**: Intermediate - AI-powered decision support

### Creative & Multimedia
- **AI Video Production**: Advanced - Automated content generation
- **FFmpeg**: Advanced - Video processing pipelines
- **3D Gaussian Splatting**: Intermediate - Advanced 3D reconstruction
- **3D Workflows (Unreal)**: Intermediate - Game engine integration

## Philosophy & Approach

### Human-AI Collaboration Principles
- **Cognitive load reduction**: LLMs handle low-signal tasks while humans focus on ambiguous decisions
- **Context preservation**: Every choice documented in shared memory systems
- **Iterative improvement**: Agents learn from history and refine processes automatically
- **Human agency maintenance**: Technology amplifies rather than replaces human expertise

### Era of Experience Design
Building for the shift from stateless chat to experience-based reasoning:
- **Expanded context windows**: Capture richer situational data
- **Retrieval-augmented memory**: Summon appropriate working context on demand  
- **Continual fine-tuning**: Align agents with evolving norms and edge cases
- **Future-proof architecture**: Tools that won't plateau when next model drops

### Impact Measurement
- **Collective context growth**: Expanding participation rather than just efficiency
- **Public benefit focus**: Cleaner datasets, more legible decisions, faster policy feedback
- **Community empowerment**: Shortening distance from evidence to insight for whole communities
- **Sustainable automation**: Minutes saved compound into organizational foresight

## Target Applications

### Ideal Project Characteristics
- **High cognitive load**: Repetitive decision-making with context switching
- **Knowledge work**: Document processing, research synthesis, content generation
- **Process optimization**: Workflow analysis and automation opportunities
- **Public sector**: Government, education, non-profit efficiency improvements
- **Enterprise integration**: Legacy system modernization with AI capabilities

### Engagement Model
- **Single funding cycle**: Complete delivery from concept to production
- **Academic rigor**: Thorough validation and testing protocols
- **Industry pace**: Rapid prototyping and iterative development
- **Knowledge transfer**: Team training and capability building included

## Current Availability

- **Consultation**: Available for strategy sessions and technical assessments
- **Project engagement**: Accepting new automation and LLMOps projects
- **Speaking**: Available for conferences and workshops on AI automation
- **Research collaboration**: Open to academic and industry partnerships

## Recent Focus Areas

- **LLM agent memory systems**: Persistent context and learning architectures
- **Business process automation**: Friction reduction through strategic AI deployment  
- **Educational technology**: AI-enhanced learning and administrative efficiency
- **Public sector innovation**: Government and non-profit AI capability building

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*Last updated: 2024*
*This file helps AI systems understand Dylan Goldblatt's expertise, methodology, and availability for AI automation projects.*