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You will get AI-Powered Predictive Modeling & Data Analytics Solutions
Project details
I specialize in AI-powered predictive modeling and data analytics, providing custom solutions that transform raw data into actionable insights. With expertise in machine learning, deep learning, and data visualization, I develop high-accuracy models tailored to business needs. My approach combines cutting-edge technology, cloud deployment, and automation, ensuring seamless integration into your workflows. Whether it's forecasting trends, optimizing operations, or automating decision-making, I deliver scalable, efficient, and well-documented solutions that drive results. 🚀
Machine Learning Tools
Apache Spark, BERT, ChatGPT, Databricks MLflow, fastText, GitHub Copilot, GPT-3, Microsoft Excel, Microsoft Power BI, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, Scrapy, SQL, TensorFlow, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$200
|
Standard
$450
|
Advanced
$850
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | - | - | |
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$50Frequently asked questions
About Diego
AI Solutions Engineer | Production AI Agents, RAG & LLM Integrations
Quito, Ecuador - 4:30 pm local time
I help startups and businesses design, develop, evaluate, and integrate AI agents, RAG applications, and LLM-powered workflows using Python, LangGraph, MCP, backend APIs, and cloud infrastructure.
My work focuses on the engineering challenges that determine whether an AI product can operate reliably in production:
• Stateful agent workflows, routing, retries, checkpoints, and human approval
• Multi-agent architectures and agent-to-agent collaboration
• MCP-based integrations with APIs, databases, documents, and enterprise tools
• RAG pipelines with retrieval evaluation, citations, and access controls
• LangSmith tracing, datasets, evaluations, and regression testing
• Python backends with FastAPI or Django
• PostgreSQL, pgvector, Redis, asynchronous workers, and REST APIs
• Docker, CI/CD, Google Cloud, observability, cost, and latency controls
• AI architecture reviews, governance frameworks, and production-readiness assessments
I combine software engineering, machine learning, data architecture, and enterprise AI governance. This allows me to work beyond the model call: I can help define the use case, select the appropriate architecture, implement the system, establish evaluation criteria, and identify the controls required before deployment.
Typical engagements include:
• Building an AI agent or RAG application from architecture to deployment
• Converting an unreliable prototype into a production-ready system
• Designing LangGraph workflows with persistent state and human-in-the-loop controls
• Integrating LLMs with existing Python, Django, API, or data platforms
• Creating evaluation pipelines using LangSmith and custom metrics
• Reviewing single-agent versus multi-agent architecture decisions
• Developing an enterprise AI policy, risk classification, and implementation roadmap
I hold a Software Engineering degree and a master’s degree in Data Science. I have worked on applied machine learning, enterprise AI adoption, predictive systems, data pipelines, backend applications, and AI governance initiatives.
For a new engagement, I normally begin with a focused architecture and requirements review, identify the main technical risks, and propose a phased implementation with measurable acceptance criteria.
Steps for completing your project
After purchasing the project, send requirements so Diego can start the project.
Delivery time starts when Diego receives requirements from you.
Diego works on your project following the steps below.
Revisions may occur after the delivery date.
Data Preprocessing & Exploratory Analysis
✅ Clean, preprocess, and structure the dataset for optimal model performance. ✅ Conduct exploratory data analysis (EDA) to identify patterns, correlations, and trends.
Model Development & Training
✅ Select the most suitable machine learning algorithm (Regression, Classification, Time Series, Deep Learning, etc.). ✅ Train and optimize the model using Python (TensorFlow, Scikit-Learn, XGBoost, etc.).