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Open to senior AI/ML roles & graduate research

Joseph Azumbil Agere · AI/ML Lead · Accra, Ghana

Building AI systemsthat actually ship.

AI/ML Lead at 4th-IR. I design and deliver multi-agent platforms, hybrid RAG, and production LLM systems, from prompt pipelines to evaluation and deployment.

View selected workGet in touch
Data-modeling time with the Autonomous Data Modeler
Weeks to hours
Less document-review time from NLP pipelines
~70%
Leads AI engineering at 4th-IR and mentors two engineers
Team lead
01About
Joseph Azumbil Agere, AI/ML Lead
Joseph Agere · Accra, Ghana

Senior AI/ML engineer with a foundation in software engineering and data science.

I design and deliver intelligent systems end to end, from agentic and multi-agent platforms to LLMs, NLP, retrieval, and the MLOps that takes them to production. My focus is solutions that are reliable, measurable, and genuinely useful to the organizations that depend on them.

I'm especially interested in advancing NLP for African languages, and in how we can apply AI to the problems closest to our communities: healthcare, agriculture, and the public services people depend on.

I also enjoy mentoring junior engineers, and I stay close to the research that keeps AI dependable and genuinely useful in real-world settings.

02Experience

Three years shipping in production.

From software delivery to leading an AI engineering team, a steady move toward harder, higher-stakes systems.

  1. AI/ML Lead · 4th-IR

    Jun 2024 - Present
    • Lead technical contact on engagements. Set team standards and mentor two junior AI engineers.
    • Established the team's model-evaluation standards: quality benchmarks, safety checks, and human-review workflows that gate AI responses before client deployment.
    • Delivering Twynity, a virtual-workforce platform where users create digital twins, each with its own persona, skills, and knowledge base, that handle tasks autonomously. Built the agent layer with LangGraph for orchestration and MCP servers for tool access.
    • Designed an Autonomous Data Modeler that turns a business spec into logical data models, then maps to existing warehouse tables or proposes new schemas, cutting data-modeling time on a recent engagement from weeks to hours.

  2. AI/ML Engineer · Inngen

    Nov 2023 - Jun 2024
    • Built Frank, a regulatory chatbot used daily by staff at the Frankfurt Health Department for compliance lookups, using LangChain, Pinecone, and FastAPI with RAG over their policy documents.
    • Built NLP pipelines for enterprise document review, cutting review time by around 70% on the main client workflow.
    • Fine-tuned and deployed domain-specific LLMs on AWS SageMaker.
    • Designed multi-step prompt pipelines with fallback handling for cases where the primary path failed.

  3. Software Developer · AfroTechnologies

    Jun 2023 - Nov 2023
    • Built a Senior High School admissions system that replaced in-person enrollment for thousands of students, cutting administration and travel time by around 60%.
    • Contributed to the deployment of a Learning Management System used by several schools for virtual teaching.
03Selected Work

Systems built for real clients.

Most of this is confidential client work, so each project is told as a case study: the problem, the approach, and the outcome.

Twynity

Client work

A virtual-workforce platform of autonomous digital twins.

Problem
A single generic assistant could not cover the range of work teams needed. They wanted task-capable digital workers, each with a distinct persona, skillset, and knowledge base.
Approach
Built the agent layer with LangGraph for orchestration and MCP servers for tool access, so each twin reasons, calls tools, and acts on its own.
Impact
Digital twins that handle real tasks autonomously across the platform.
LangGraphMCPMulti-agentLLMs

Compliance & KYC Agents

Client work

Production agents for MiFID II checks, KYC, and obligation extraction.

Problem
Regulated enterprises needed compliance and onboarding checks that were accurate, auditable, and fast enough for real workloads.
Approach
Designed prompt pipelines with multi-step fallback chains and structured output, gated by explicit evaluation and human-review standards before deployment.
Impact
In production with enterprise clients.
LLMsStructured outputPrompt pipelinesEvaluation

Autonomous Data Modeler

Client work

From a business spec to logical data models, automatically.

Problem
Data modeling on each engagement was slow and manual, holding up everything downstream.
Approach
Built a system that takes a business spec, generates logical data models, and either maps to existing warehouse tables or proposes new schemas.
Impact
Cut data-modeling time on a recent engagement from weeks to hours.
LLMsData modelingWarehousesSchema design

Hybrid RAG Pipelines

Client work

Retrieval that finds the chunks plain vector search misses.

Problem
Standard dense vector search kept missing the right context on harder corpora.
Approach
Combined dense and sparse retrieval with a feedback-memory loop so the pipeline learns from what it gets wrong.
Impact
Adopted across several client deployments.
Hybrid retrievalPineconeWeaviateFeedback memory

Frank

Client work

A regulatory chatbot for the Frankfurt Health Department.

Problem
Staff needed fast, reliable compliance lookups across a large body of policy documents.
Approach
Built RAG over their policy corpus with LangChain, Pinecone, and FastAPI, plus prompt pipelines with fallback handling.
Impact
Used daily by department staff.
LangChainPineconeFastAPIRAG
04Capabilities

The stack I build with.

Depth across the modern AI engineering stack, from agents and retrieval down to the MLOps that keeps it all running.

LLMs & Generative AI

  • OpenAI
  • Anthropic Claude
  • Mistral
  • LoRA / domain fine-tuning
  • Prompt engineering
  • Model output evaluation

Agents

  • LangGraph
  • LangChain
  • LlamaIndex
  • MCP servers
  • Multi-agent orchestration

RAG & Retrieval

  • Hybrid retrieval
  • Pinecone
  • Weaviate
  • Embedding pipelines
  • Feedback memory

ML & NLP

  • PyTorch
  • TensorFlow
  • scikit-learn
  • NLP pipelines
  • Model evaluation

MLOps & Cloud

  • AWS
  • AWS SageMaker
  • Azure
  • Docker
  • CI/CD
  • Terraform

Backend & APIs

  • FastAPI
  • Django
  • Flask
  • GraphQL
  • REST
  • PostgreSQL
  • MongoDB

Languages

  • Python
  • JavaScript
  • PHP
  • C++
05Education & Research

Foundations &where I'm headed.

A computer-science grounding, an active research interest, and a habit of teaching what I learn.

B.Sc. Computer Science

2019 - 2023

University for Development Studies · Ghana

Coursework
  • Artificial Intelligence
  • Data Structures & Algorithms
  • Software Engineering
  • Cloud Computing
  • Linear Algebra
  • Algorithm Design & Analysis

Research interests

  • Low-resource and African-language NLP
  • Multi-agent LLM systems
  • Retrieval-augmented generation
  • Evaluation & safety of generative models
  • Efficient fine-tuning (LoRA)

Certifications

  • LLMOps
  • End-to-end MLOps with Azure Machine Learning
  • Apache Airflow 3 Fundamentals (Astronomer)

Beyond work

IndabaX Ghana 2025

Ghana Data Science Summit · Ashesi University

Attended workshops on multimodal LLMs and LoRA fine-tuning.

Mentor, Teens in AI (Africa)

Since 2024

Mentoring the next generation of African AI builders.

First Runner-Up, FinTech Hackathon 1.0

Trestle Academy Ghana · 2022

Co-built a virtual-card platform with AI fraud prevention for cross-border transactions.

06Contact

Let's build something worth shipping.

Open to senior AI/ML roles, client engagements, and graduate research conversations. The fastest way to reach me is email.

agerejoseph448@gmail.comLinkedInGitHub
Accra, Ghana(+233) 541 433 448