You will get an Agent Build Sprint: one AI use case shipped to production in 4-6 weeks
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Project details
This is where your AI agent goes from idea to production. In 4-6 weeks I take a single, well-defined use case and ship a working agent inside your stack, not a prototype, not a demo, but something your team can actually run. I'm a Staff Engineer specializing in production LLM agents: local and open models (LLaMA and similar), on-prem or private-cloud deployment, MCP-based tool integration, and architectures that keep your data inside your infrastructure. No OpenAI dependency unless you specifically want it. The sprint covers scoping, build, integration with your systems and data, testing, deployment, and the documentation your team needs to own it. You get clean source code, a deployed agent, and a clear handover, with EU AI Act considerations built in from day one. Ideal for engineering-led companies who want real AI capability shipped, not another proof of concept that never leaves the lab.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, NVIDIA AI Platform, PyTorchAI Models
LLaMAWhat's included $12,500
These options are included with the project scope.
$12,500
- Delivery Time 42 days
- Number of Revisions 2
- AI Model Integration
- Database Integration
- MLOps
- Model Deployment
- Model Documentation
- Model Testing & Optimization
- Source Code
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MK
Markus K.
May 8, 2026
PHP Developer - Admin Tool Interfaces
One of the most reliable engineers I've worked with. Terry has been a key part of several projects over the last year, including AWS infrastructure security, browser extension monitoring, and scraping infrastructure. Consistently delivered without needing to be managed. Hand him a problem and he comes back with a working solution and a clear explanation. Strong technical range, fast turnaround, honest about tradeoffs. Hire him if you need a senior engineer who can run a project on their own.
IA
Ibtisam A.
Jan 28, 2025
Backend Developer (PHP) with DevOps Understanding
Highly skilled DevOps engineer with expertise in CI/CD pipelines, cloud infrastructure, and automation. His ability to streamline workflows, ensure seamless deployments, and maintain system reliability is outstanding.
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Aug 20, 2024
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Jim L.
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Khaled K.
Dec 27, 2023
Fix HMAC PHP
Excellent developer
About Ogbemudia Terry
AI Engineer | Production LLM Agents, On-Prem & EU AI Act-Ready
100%
Job Success
Bergisch Gladbach, Germany - 5:16 pm local time
Most AI projects die in pilot purgatory: a few Slack bots, a Confluence plugin, nothing actually running. I take one real use case and get it into production: local LLM deployment, RAG, MCP tool integration, runbooks, and audit logs - on your infrastructure, so your data never has to leave it.
This matters most for engineering-led companies operating under European data and compliance constraints. Your telemetry, customer data, and postmortems stay on-prem. Every use case gets an EU AI Act risk classification. No vendor lock-in - MCP standard, swappable models.
13 years of software engineering, Top Rated Plus, 100% Job Success. Founder of TraceKit (production debugging/APM) and funkel.ai. I work the way it should be sold: fixed price, fixed scope, one engineer who strategizes, builds, and hands off cleanly.
Three ways to start:
• AI Readiness Audit - 1 week, one PDF, one use case ranked and ready to build
• Agent Build Sprint - 4–6 weeks, one use case to production, fixed price
• Embedded AI Engineer - fractional Staff Engineer on retainer inside your team
If you want production AI without the usual headaches, let's talk.
Steps for completing your project
After purchasing the project, send requirements so Ogbemudia Terry can start the project.
Delivery time starts when Ogbemudia Terry receives requirements from you.
Ogbemudia Terry works on your project following the steps below.
Revisions may occur after the delivery date.
Week 1: Scope & architecture
We lock the use case, success criteria, data, and a local/on-prem LLM architecture for your stack.
Weeks 2-5: Build & integrate
I build the agent, integrate it with your systems and data via MCP, and test against the success criteria.