Work Projects Code Graph Code Brain Insights Consulting Services Case Studies About About me Skills Experience Contact
1,439 repositories, positioned by meaning Open the atlas →
Available for new projects

Moses Yebei - AI Engineer · SRE · AI Platform Engineer

I build and run AI systems in production.

To learn how good software is put together, I built Glossa: a knowledge graph of 1,440 open-source repositories. It maps how each one is structured, what it depends on and how healthy it is. Then a model writes a plain-English explanation of each repo from those checked facts only, so every number traces back to a real analysis. It refreshes every two hours.

0+
Repositories
0+
Languages
0
Files Analyzed
0
Modules Mapped
0
Findings Surfaced
Scroll

How Glossa reads a repository

Seven stages turn a repository I have never opened into something I can argue about - what it is built from, what it commits to, and where those commitments are already costing it. A gloss is commentary bound to a source text, which is what a briefing here is: the model writes the prose, the checks supply every fact in it. 56 scripts, no model in the grading path, 188 assertions across 11 suites holding the claims in place. Every stage writes a file the next one reads, so any number on this site can be walked back to the check that produced it.

  1. Census

    Enumerates every repository and its facts - language, size, licence, lockfile, last push - into a single feed.

    forks.json
  2. Briefing

    A model writes the article from the extracted facts alone. The prompt forbids naming an edge, a path or an effect that is not in them, and says so where the wiring has not been mapped yet.

    blog/<slug>.html
  3. Structure

    Walks the file tree and parses the sources into a module graph: imports, edges, cycles, depth.

    structure/<id>.deep.json
  4. Supply

    Resolves dependency manifests and matches them against the OSV advisory database.

    data/osv.json
  5. Hygiene

    62 checks across CI, tests, secrets, runtime and supply chain - each one a finding with a severity and a count.

    data/hygiene.json
  6. Grade

    Charges those findings to 8 weighted axes. Deterministic: the same inputs grade identically, which is the only reason it is safe to publish.

    data/grades.json
  7. Meaning

    Embeds each repository, draws semantic and dependency-derived edges, and clusters them by modularity rather than by name.

    data/relations.json
  8. Publish

    Emits a declared schema and an llms.txt traversal protocol, so an agent can answer a question here for about a kilobyte instead of parsing the whole index.

    data/schema.json

Not analysed never renders as clean. A repository graded without module-level analysis is marked partial and loses saturation on the graphs, because a neutral score on an axis nothing measured is a false claim.

Unaudited is not a grade. It gets a colour outside the scale entirely, not the bad end of it.

Every claim states its provenance. Facts are tagged extracted or inferred per fact, not per page - a dependency edge names its packages and can be checked; a similarity edge is a number from a model and says so.

Reading open source at scale

Every number below is a live query over the estate, not a claim. Follow one.

Reading the index…

Recent Activity
Loading activity…
Less
More
Loading contribution data...

Who is behind all this

The short version, before the evidence below.

I am an AI engineer working across the full stack of a system, from data pipelines and model work through to the interface someone actually uses. Most of my time goes to retrieval, embeddings and knowledge graphs, with a background in computer vision and deep learning.

The projects, Code Graph and Code Brain sections are not screenshots. They are a live system I built that reads every repository I own or have forked, maps them into a semantic space, writes a technical briefing for each and flags the security, dependency and deployment gaps it finds. It refreshes itself every two hours without anyone touching it.

I am open to senior AI and ML engineering roles, and take selected consulting work alongside. Based in Nairobi, working with teams anywhere.

See what it found → Get in touch →

What I Work With

Domains and technologies I work across - from governance to graphs to production

AI Governance & Readiness

AI Policy, Responsible AI, Data Governance, Risk & Compliance, Readiness Assessment

Machine Learning

PyTorch, Scikit-learn, XGBoost, LightGBM, LSTM, Time-Series Forecasting

AI Agents as Graphs

LangGraph, LangChain, LiteLLM, Langfuse, RAG Pipelines, graph-structured Multi-Agent Systems

Knowledge Graphs & GraphRAG

GraphRAG, Neo4j, FalkorDB, NetworkX, Vector DBs (Pinecone, FAISS, Chroma, Weaviate, pgvector)

Cloud & MLOps

AWS, Azure ML, GCP Vertex AI, Docker, Kubernetes, MLflow, Weights & Biases

Full-Stack & APIs

FastAPI, Next.js, PostgreSQL, Redis, Temporal, OpenTelemetry

Data Engineering

PySpark, ETL Pipelines, A/B Testing, Statistical Modeling, EDA

What I have built, and where it ran

Employers and clients are described by sector rather than named, and the work is stated as what I did and what it changed. Happy to go through specifics in a conversation.

Oct 2025 - Present · Remote

Founding AI/Data Engineer

Developer-tooling startup · US

Founding engineer on an automated software-analysis platform, owning it from architecture to production. Designed a three-layer knowledge-graph pipeline (FalkorDB/Cypher, 149 entity types) feeding 7 specialist AI agents - each served a tailored graph slice instead of raw context, which cut token spend and lifted output quality. Built 20+ Temporal workflows and 120+ activities covering 20+ languages, with Kafka streaming, ClickHouse analytics, and full observability on production GKE.

Knowledge Graphs AI Agents as Graphs Temporal FalkorDB Kubernetes / GKE
Jan 2025 - Sep 2025 · Remote

AI Engineer & Software Architect

Careers-tech platform · Kenya

Owned a user-facing, full-stack AI platform end to end - requirements to production with active users - automating the job-application lifecycle via coordinated AI agents. Built a FastAPI backend with a central multi-agent orchestrator, a FAISS-backed RAG pipeline, and CV parsing / intent routing with scikit-learn, spaCy, and NLTK. Hardened with a circuit-breaker pattern, dependency injection, and clear separation of concerns.

Multi-Agent Systems FastAPI RAG / FAISS System Design
Aug 2022 - Dec 2024

AI & Forensic Engineer, Litigation Data Solutions

Litigation data & e-discovery firm · US

Technical lead on forensic and e-discovery delivery for high-stakes litigation, owning the AI roadmap and aligning it with client needs. Built an NLP/ML pipeline processing 1M+ documents at 99% accuracy and a speech-to-text pipeline at 99% accuracy. Architected pipelines across Relativity, CloudNine Law, and Microsoft Purview - improving process efficiency 30% and cutting retrieval time 25%.

NLP at Scale E-Discovery Data Pipelines
Jan 2021 - Nov 2023

Data Science & Analytics Consultant

Independent · Clients in six sectors

Consulted across fintech, banking, agriculture, supply chain, e-commerce, and healthcare. Built a bank fraud-detection model reducing fraudulent transactions 75%, AI chatbots lifting lead engagement 60%, and predictive solutions improving client operational efficiency 40%. Delivered real-time analytics dashboards and hands-on training for non-technical teams.

Fraud Detection Predictive Modeling Fintech

What I'm Building

My projects and interesting forks - AI-generated insights with detected bugs & SDLC issues, updated every 2 hours

A living feed of my repositories and notable forks. Each one gets an automated technical briefing - what it does, how it's built, and a code-health review flagging likely bugs and SDLC/code violations.

Explore the full portfolio

How I Can Help

Five ways I help, across the full lifecycle from governance to shipped systems. See all services →

AI Governance & Policy

Guardrails before you scale.

Learn more →

AI & Data Readiness

Most AI projects fail on readiness, not modeling.

Learn more →

RAG Pipeline Development

Demos are easy; production RAG that stays accurate is not.

Learn more →

LLM Training & Fine-Tuning

When prompting and RAG hit their limits.

Learn more →

Open To

Exploring new opportunities in AI/ML

Full-Time Roles

AI/ML Engineer or Data Scientist positions at innovative companies.

Remote US Canada Australia

Academic Programs

Research opportunities and graduate programs in AI/ML.

Japan US China Canada

GPU Scholarships

Compute grants and AI research scholarships.

NVIDIA Google AWS Alibaba

Let's Connect

Have a project in mind? I'd love to hear from you.

Get in Touch

I'm always interested in hearing about new projects, opportunities, or just having a conversation about AI and technology. Feel free to reach out through the form or connect with me directly.