Thomas Nganga
Nairobi, Kenya
Thomas Nganga
Artificial intelligence and ML engineer
Category : Artificial intelligence (AI)
I am a self-taught Systems Architect and Full-Stack Engineer specializing in local-first AI systems and deterministic data architecture.
As an AWS AI & ML Scholar, I bridge the gap between complex cloud infrastructure and privacy-focused, independent AI implementations.My expertise lies in building "truth-gated" systems that eliminate AI hallucinations through Ontology-guided extraction and Knowledge Graphs (Neo4j). Whether you need an automated trading bot driven by GANs or a secure RAG pipeline that ensures 0% data leakage to the cloud, I design for efficiency and logic-driven execution.
Key Specializations:
1.Local-First AI: Building robust RAG pipelines using Incremental Knowledge Representation (IKR).
2.Model Observability: Implementing OpenTelemetry and MCP to trace logic and reduce bug detection time.
3.Complex Data Engineering: Advanced SQL/Cypher development for triple extraction and knowledge graph reasoning.Trading Systems: Developing algorithmic bots with Volume Profile analysis and custom regression logic.
4.SkillsAI/ML: RAG Pipelines, Triple Extraction, LLM Fine-tuning (AWS SageMaker), Generative Adversarial Networks (GANs).Data Architecture: Neo4j (Graph), ChromaDB (Vector), SQL Development, NoSQL (MongoDB), Data Ingestion Pipelines.
5.Engineering: OpenTelemetry, Model Context Protocol (MCP), Docker, CI/CD, Playwright.
6.Languages: Python (PyTorch, Scikit-learn), TypeScript, Cypher, SQL.
As an AWS AI & ML Scholar, I bridge the gap between complex cloud infrastructure and privacy-focused, independent AI implementations.My expertise lies in building "truth-gated" systems that eliminate AI hallucinations through Ontology-guided extraction and Knowledge Graphs (Neo4j). Whether you need an automated trading bot driven by GANs or a secure RAG pipeline that ensures 0% data leakage to the cloud, I design for efficiency and logic-driven execution.
Key Specializations:
1.Local-First AI: Building robust RAG pipelines using Incremental Knowledge Representation (IKR).
2.Model Observability: Implementing OpenTelemetry and MCP to trace logic and reduce bug detection time.
3.Complex Data Engineering: Advanced SQL/Cypher development for triple extraction and knowledge graph reasoning.Trading Systems: Developing algorithmic bots with Volume Profile analysis and custom regression logic.
4.SkillsAI/ML: RAG Pipelines, Triple Extraction, LLM Fine-tuning (AWS SageMaker), Generative Adversarial Networks (GANs).Data Architecture: Neo4j (Graph), ChromaDB (Vector), SQL Development, NoSQL (MongoDB), Data Ingestion Pipelines.
5.Engineering: OpenTelemetry, Model Context Protocol (MCP), Docker, CI/CD, Playwright.
6.Languages: Python (PyTorch, Scikit-learn), TypeScript, Cypher, SQL.
Working hours
- Monday:08h00 To 18h00
- Tuesday:08h00 To 18h00
- Wednesday:08h00 To 18h00
- Thursday:08h00 To 18h00
- Friday:08h00 To 18h00
- Saturday:Not available
- Sunday:Not available
![I am a high-integrity Systems Architect and Full-Stack Engineer specializing in the development of autonomous, local-first AI ecosystems.]()
I am a high-integrity Systems Architect and Full-Stack Engineer specializing in the development of autonomous, local-first AI ecosystems.35 $ - Per hourI am a high-integrity Systems Architect and Full-Stack Engineer specializing in the development of autonomous, local-first AI ecosystems. As an AWS AI & ML Practitioner, I bridge the gap between ...
Core AI/ML Proficiency: Demonstrated understanding of machine learning lifecycle stages, including data preparation, model selection, and deployment using AWS-native tools like SageMaker.
Practical Project Execution: Successful completion of hands-on builds, specifically in Data Analysis and AI Productivity Applications, proving the ability to transform theoretical concepts into functional software.
Strategic Resource Management: Mastery of selecting the most efficient and cost-effective AWS services (such as Bedrock, Rekognition, or Polly) for specific business use cases.
Security and Ethics: Knowledge of AWS best practices for responsible AI development, including data privacy, model bias mitigation, and secure cloud infrastructure management.
Professional Validation: Earning a verified credential from Udacity/Accenture that confirms technical readiness to architect and manage modern AI-driven solutions in a cloud-ready environment.
Practical Project Execution: Successful completion of hands-on builds, specifically in Data Analysis and AI Productivity Applications, proving the ability to transform theoretical concepts into functional software.
Strategic Resource Management: Mastery of selecting the most efficient and cost-effective AWS services (such as Bedrock, Rekognition, or Polly) for specific business use cases.
Security and Ethics: Knowledge of AWS best practices for responsible AI development, including data privacy, model bias mitigation, and secure cloud infrastructure management.
Professional Validation: Earning a verified credential from Udacity/Accenture that confirms technical readiness to architect and manage modern AI-driven solutions in a cloud-ready environment.
This credential demonstrates a professional baseline for interacting with and deploying AI solutions within the Microsoft ecosystem.
Generative AI Literacy: Mastery of the core concepts of Generative AI, including Large Language Models (LLMs) and their role in modern business productivity.
Copilot & Azure Integration: Proficiency in using Microsoft Copilot and understanding the potential for AI integration within Azure services.
Prompt Engineering Foundations: Demonstrated ability to craft effective prompts to improve the quality, accuracy, and relevance of AI-generated outputs.
Ethical AI Implementation: A solid understanding of Microsoft's Responsible AI principles, focusing on fairness, reliability, and security.
IOE (Institute of Engineers) AI Certification
This accomplishment highlights the intersection of AI and rigorous engineering standards.
Engineering-Grade Problem Solving: Demonstrated ability to apply AI methodologies to traditional engineering challenges, emphasizing systematic and deterministic approaches.
Standardization & Compliance: Mastery of the engineering standards required for AI integration, ensuring that automated systems meet professional reliability benchmarks.
Cross-Domain Competence: Proof of the technical versatility required to bridge the gap between high-level data science and practical, on-the-ground engineering applications.
Generative AI Literacy: Mastery of the core concepts of Generative AI, including Large Language Models (LLMs) and their role in modern business productivity.
Copilot & Azure Integration: Proficiency in using Microsoft Copilot and understanding the potential for AI integration within Azure services.
Prompt Engineering Foundations: Demonstrated ability to craft effective prompts to improve the quality, accuracy, and relevance of AI-generated outputs.
Ethical AI Implementation: A solid understanding of Microsoft's Responsible AI principles, focusing on fairness, reliability, and security.
IOE (Institute of Engineers) AI Certification
This accomplishment highlights the intersection of AI and rigorous engineering standards.
Engineering-Grade Problem Solving: Demonstrated ability to apply AI methodologies to traditional engineering challenges, emphasizing systematic and deterministic approaches.
Standardization & Compliance: Mastery of the engineering standards required for AI integration, ensuring that automated systems meet professional reliability benchmarks.
Cross-Domain Competence: Proof of the technical versatility required to bridge the gap between high-level data science and practical, on-the-ground engineering applications.
- AI FLUENCY CERTIFICATE 21/04/2026
![AI FLUENCY CERTIFICATE]()
- AWS AI PRACTITIONER CERTIFICATE 20/04/2026
![AWS AI PRACTITIONER CERTIFICATE]()
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