You will get an end to end ML pipeline built from raw data/documents to production API
Project details
You will get a machine learning pipeline that goes from raw data all the way to a production API your product and engineering teams can actually use with monitoring, logging, and continuous improvement built in.
Most ML work stops at the notebook. The model trains well, evaluates well, and then sits there because nobody has turned it into something a real application can call reliably. That gap from trained model to production system, exactly what this covers.
Real results: 50% improvement in model inference speed, 40% reduction in API response times, end-to-end ML pipelines built across healthcare diagnostics, enterprise platforms, and SaaS products. Monitoring systems for model health and anomaly detection built and deployed in production. AWS Generative AI Developer, Google Cloud ML Engineer, and Azure AI Engineer certified
Most ML work stops at the notebook. The model trains well, evaluates well, and then sits there because nobody has turned it into something a real application can call reliably. That gap from trained model to production system, exactly what this covers.
Real results: 50% improvement in model inference speed, 40% reduction in API response times, end-to-end ML pipelines built across healthcare diagnostics, enterprise platforms, and SaaS products. Monitoring systems for model health and anomaly detection built and deployed in production. AWS Generative AI Developer, Google Cloud ML Engineer, and Azure AI Engineer certified
Machine Learning Tools
Apache Spark, Apache Spark MLlib, Azure Machine Learning, ChatGPT, deeplearn.js, GitHub Copilot, Google AutoML, Google Sheets, GPT-3, MATLAB, Microsoft Excel, Microsoft Power BI, MLflow, NumPy, Open Neural Network Exchange, OpenCV, pandas, PyMC, Python, PyTorch, SPSS, SQL, Tableau, TensorFlow, Vertex AIWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$3,000
|
Advanced
$7,000
|
|---|---|---|---|
| Delivery Time | 7 days | 15 days | 28 days |
Number of Revisions | 1 | 3 | Unlimited |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 1 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | |||
Source Code | - | - |
About Mo
AI/ML Engineer | Agentic AI & Autonomous Workflows | LangGraph & LLMs
Woodbridge, United States - 1:27 am local time
I build lean, high-throughput LangGraph architectures that cut API latency by 40% and double inference speeds so your AI scales without burning your run-rate.
Bridging the gap between brittle LLM prototypes and deterministic, enterprise-grade AI. I architect stateful multi-agent workflows, low-latency RAG pipelines, and scalable FastAPI backends that integrate directly into your databases, CRMs, and internal systems.
CORE AI CAPABILITIES
• Agentic Workflows & Multi-Agent Orchestration: Designing stateful, fault-tolerant agent architectures using LangGraph, CrewAI, and the Model Context Protocol (MCP) for complex decision-making.
• Enterprise RAG & Advanced Retrieval: Implementing hybrid search, GraphRAG, dynamic re-ranking, and context compression to minimize hallucinations and cut token costs.
• Production AI Backends & Microservices: Architecting high-throughput FastAPI systems with async execution, streaming responses, structured JSON output, and failover routing.
• LLM Observability & Evaluation: Setting up automated evaluation pipelines, prompt optimization, telemetry, and monitoring using LangSmith to ensure production reliability.
TECH STACK I WORK WITH AND HANDS ON
• Agent Frameworks:
LangGraph, CrewAI, Model Context Protocol (MCP), AutoGen
• LLMs & RAG Engine:
OpenAI API, Anthropic Claude, Gemini, LlamaIndex, LangChain
• Vector Stores & Databases:
Pinecone, Qdrant, Weaviate, PGVector, Redis
• Backend & Cloud:
Python (Async), FastAPI, PostgreSQL, Docker, AWS, GCP, CI/CD
• Observability & Eval:
LangSmith, DeepEval, Guardrails AI
⚠️ THE 3 PRODUCTION FLAWS I CONSTANTLY FIX IN EXISTING REPOS
If your AI system worked in local dev but broke in production, you likely hit one of these:
1. "Naive RAG Syndrome": Dumping raw chunks into vector databases without dynamic re-ranking or metadata filtering, resulting in high latency and low accuracy.
2. Unhandled State Collapses: Single-prompt agents getting stuck in infinite loops without state machine controls or deterministic fallbacks.
3. Token Bloat: Over-stuffing system prompts with uncompressed context, driving up monthly API bills by 300%.
I restructure these brittle setups into deterministic, production-ready microservices built for heavy load.
💡 GOT A SLOW OR EXPENSIVE AI PIPELINE? GET A 5-MINUTE AUDIT
Already have an AI agent or RAG system in staging/production, but it's slow, expensive, or hallucinating?
Instead of scheduling a long discovery call, send me a quick message with a brief overview of your tech stack or prompt/RAG architecture. I will send back a free, personalized 5-minute video breakdown showing:
1. Where your pipeline is leaking token costs.
2. How to shave 30%+ off your API latency.
3. The exact architectural tweak needed for deterministic outputs.
Click "Invite to Job" or send a message to request your audit or discuss a new project setup.
Steps for completing your project
After purchasing the project, send requirements so Mo can start the project.
Delivery time starts when Mo receives requirements from you.
Mo works on your project following the steps below.
Revisions may occur after the delivery date.
Data Audit & Pipeline Design
Data sources reviewed, quality assessed, preprocessing steps defined. Pipeline architecture designed before any code is written
Data Pipeline
Cleaning, transformation, feature engineering, and train/test split built as a reproducible pipeline not a one off notebook