You will get a production-ready AI agent with MCP, custom tools, and handoffs
Rising Talent
Project details
You will get a production-ready AI agent — built with Claude or OpenAI — with custom tools, MCP integration, streaming, guardrails, and full deployment. I lead AI engineering at LexisNexis (20+ shipped systems) and founded a healthcare AI startup serving 13 clinics at 98% accuracy. I was selected for the Anthropic Hackathon (13,000 applicants). I move fast, communicate clearly, and ship agents that work in production — not demos that fall apart.
AI Algorithms
Large Language ModelAI Applications
AI-Generated Code, Conversational AIAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$750
|
Standard
$2,500
|
Advanced
$6,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 28 days |
Number of Revisions | 2 | 3 | 5 |
AI Model Integration | |||
Batch Normalization | |||
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | |||
MLOps | |||
Model Deployment | |||
Model Documentation | |||
Model Monitoring | |||
Model Testing & Optimization | |||
Model Tuning | |||
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
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JI
Jk I.
Feb 19, 2026
30 minute consultation
Tyler is very knowledgeable and a great communicator. He provided tremendous value during our consultation. 10/10 would recommend!
TC
Tom C.
Aug 18, 2025
Python programming expert
About Tyler
Senior AI Engineer | AI Agents, RAG, MCP & LLM Systems
Blanchard, United States - 11:45 pm local time
I design and ship end-to-end AI products: document ingestion, hybrid retrieval and reranking, source-grounded citations, tool-calling agents, MCP servers, evaluations, and the full web/backend infrastructure around them.
What I can build for you:
• Production AI agents with tool use, handoffs, browser automation, and guardrails
• RAG pipelines with ingestion, chunking, embeddings, vector/hybrid search, reranking, and citations
• MCP servers, proxies, and agent-tool integrations
• LLM evaluation, observability, prompt/version management, and model routing
• Full-stack AI apps with Python/FastAPI, TypeScript/Node.js, Next.js, PostgreSQL, Docker, and AWS
Selected results:
• Led technical direction for 20+ production AI workflows at LexisNexis and architected shared LLM infrastructure for retrieval, prompt/version management, and model routing
• Built a production ML classifier that improved accuracy by 9.5 points, saving about 50 hours and $25K net per week
• Built healthcare AI used by 13 clinics with 98% clinical-code extraction accuracy
• Automated ETL and browser workflows for Fortune 500 clients, reaching about 90% automation and protecting $2.2M in revenue
• Built toolmux, an MCP proxy that cuts agent tool-schema context by up to 96%
• Built Sandcastle, a sandbox for untrusted JavaScript benchmarked at 380K ops/sec
Core stack: Python, TypeScript, OpenAI, Claude/Anthropic, Azure OpenAI, OpenAI Agents SDK, Claude Agent SDK, Vercel AI SDK, LangChain, Playwright, PostgreSQL, Pinecone, Docker, and AWS.
I move quickly, communicate clearly, and care about reliability, measurement, and maintainable code. Send me the system you need to build—or the one that isn't working yet—and I'll help you get it into production.
Steps for completing your project
After purchasing the project, send requirements so Tyler can start the project.
Delivery time starts when Tyler receives requirements from you.
Tyler works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & scope
30-min call to align on use case, tools, success criteria. I'll confirm scope and timeline in writing before any code.
Prompt & tool design
Design the prompt, tool schemas, and handoff logic. Share a short plan doc for review before building.