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You will get a fully implemented AI dev workflow for your team: agents and guardrails

Wes M.Status: Offline
Wes M. Wes M.
Rising Talent
Preview image

Let a pro handle the details

Buy Generative AI services from Wes, priced and ready to go.
Wes M.Status: Offline
Wes M. Wes M.
Rising Talent
Preview image

Let a pro handle the details

Buy Generative AI services from Wes, priced and ready to go.

Project details

I set up a disciplined AI-assisted development workflow for your team - agent roles, reusable
skills, guardrail hooks, and context and token conventions - so AI-assisted coding stays fast and
reliable, instead of ad hoc and drift-prone. Final product will be a deployable harness you can tailor per team, repo or project that will be fully reusable.
AI Algorithms
Large Language Model
AI Applications
AI Mobile App Development, AI-Generated Code, AIOps
AI Development Language
Python
AI Models
DALL-E, GPT-4

What's included $1,500

These options are included with the project scope.

$1,500
  • Delivery Time 7 days
  • Number of Revisions 1
    • AI Model Integration
    • Detailed Code Comments
    • Model Deployment
    • Model Documentation
    • Prompt Engineering
    • Setup File

Frequently asked questions

Wes M.Status: Offline

About Wes

Wes M.Status: Offline
AI Architect & Engineer - Agents, RAG, MCP, Bedrock, Durable Workflows
Coldspring, United States - 10:08 pm local time
AI architect and engineer. I ship production GenAI - AI agents, RAG, model routing, MCP, and AWS Bedrock, AI SDK, Durable Workflows - and the agentic dev workflows that make teams faster. I built a full PM / Architect / Engineer / QA agent harness with subagents, skills, and guardrails.

What I help with:

Build AI into your product: AI agents and agentic workflows, RAG over your own data, context engineering and prompt caching, model routing and LLM gateways, MCP servers and integrations, AWS Bedrock and provider AI SDKs, memory (vector and graph), SSE streaming, durable background workflows (queues, scheduling, retries), guardrails, and evals and observability.

Set up AI-assisted development: agent harnesses, subagent pipelines, token and context optimization, autonomous workflows, and the patterns (and anti-patterns) that keep AI-assisted teams fast and consistent.

Architecture and advisory: get unstuck from prototype to production with eval strategy, cost and latency control, security and guardrails, and a clear maturity roadmap.

Selected work:

- Agentic AI health-coaching platform (HIPAA, AWS): a tool-using AI coach that reasons over a user's biomarker time-series, with model routing, SSE streaming, pgvector evidence retrieval, and full compliance infrastructure (KMS encryption, audit logging, row-level security) on ECS and CDK.

- LLM cost-optimization proxy (Rust): a byte-stable gateway in the request path that meters token spend and applies cache-safe optimizations without ever breaking the provider prompt cache, with per-request cache-loss attribution and model routing.

- Agentic dev harness (my own framework): a context-frugal PM / Architect / Engineer / QA pipeline with role subagents, on-demand skills, guard hooks (real end-to-end gates, no untraceable commits), and a strict context budget. Production-grade agent orchestration end to end.

- Also: an autonomous consumer-commerce agent (visual semantic search with CLIP and pgvector) and an explainable, risk-gated trading assistant (LLM news-sentiment plus rigorous walk-forward evaluation).

How I work:

- Real end-to-end verification over demos. I boot it and prove it works.
- Guardrails, evals, and observability from day one; cost and context treated as budgets.
- Tight scope, clear communication, production quality: typed APIs, no swallowed errors, no shortcuts that bite later.

Tech I use: Python, Rust, TypeScript, LLM APIs (Claude, OpenAI, Gemini), agentic tool-use, MCP (Model Context Protocol), RAG and vector search (pgvector, TimescaleDB), model routing and LLM gateways, AWS (Bedrock, ECS), and evals and observability.

If you are adding AI to a product, or leveling up how your team builds with AI, send a short note with what you are trying to ship and I will reply with the fastest credible path to production.

Steps for completing your project

After purchasing the project, send requirements so Wes can start the project.

Delivery time starts when Wes receives requirements from you.

Wes works on your project following the steps below.

Revisions may occur after the delivery date.

Client purchases the project and send the requirements.

Client provides high level overview, expected outcomes and timing.

Approval or Rejection

Based on step one the project is accepted or denied.

Review the work, release payment, and leave feedback to Wes.