Muhammad Abiodun Sulaiman
Machine learning engineer and bioinformatician. I build agentic AI systems and production ML pipelines, and I work across computational genomics, statistical modelling and clinical AI.
About
I work in two areas: building AI systems, and computational biology.
Most of my time goes to engineering: LLM services and agentic systems, Model Context Protocol servers, ML pipelines, and the backend and cloud infrastructure they run on.
The rest is research. I use statistics and machine learning on genomic and clinical data: variant interpretation, pharmacogenomics in populations that reference databases underrepresent, and predictive models for health data. vartriage, a Python library I wrote for classifying clinical variants from VCF files, came out of that work. It streams whole-genome files with more than four million variants.
I trained as a statistician, so I usually start from the method and then build something that runs on real data.
Tools I use
ML and LLM systems
Backend and APIs
DevOps and cloud
Genomics and statistics
Experience
MLOps / LLMOps Engineer
I build and run the AI service behind Slice24, a live product: a FastAPI microservice that generates and improves user content.
- Built a router that picks a model per task and fails over between LLM providers, with a circuit breaker per provider so one outage does not stall requests.
- Built retrieval over a vector store with a content-ingestion pipeline, and an in-product assistant on top of it.
- Built the media generation layer, spreading image, video and audio jobs across providers within a spending limit.
- Added content moderation, PII detection and prompt-injection checks, and an async job processor with timeouts, recovery and idempotent retries.
- Added Prometheus metrics, structured logging, rate limiting and security headers, and ship the service through Docker and CI/CD.
AI/ML Engineer
Built multi-agent systems with LangChain, LangGraph and LlamaIndex, calling models from OpenAI, Anthropic, Mistral, Gemini and Groq over vector databases.
- Wrote a Model Context Protocol (MCP) server from scratch, so agents reach databases and tools through one standard protocol.
- Built retrieval-augmented generation over Pinecone and FAISS, and validated model output with Pydantic and Guardrails before it reached users.
- Reworked prompts to use fewer tokens per request.
- Packaged the agent services as containers and deployed them to Kubernetes through CI/CD.
MLOps Engineer / DPI Solutions Architect
Built an agentic assistant that recommends a technology stack by talking a project through, served from a FastAPI backend with a vector-backed knowledge base.
- Automated model training, versioning and deployment on AWS with MLflow, Docker, Kubernetes and GitHub Actions.
- Set up automated retraining and drift detection for deployed models.
- Built scenario-analysis and decision-support tools for health data, including custom apps inside the DHIS2 health-information platform.
- Led a small team of data and cloud engineers.
Data/Cloud DevOps Engineer
Ran the containers, monitoring and cloud security for data pipelines and ML services on AWS.
- Built and deployed models for customer segmentation, fraud detection and predictive analytics.
- Built REST APIs with FastAPI and Pydantic, deployed on AWS ECS.
- Set up monitoring with Prometheus, Grafana and the Elastic Stack, and automated data-validation checks.
- Configured EC2 auto-scaling, and managed IAM, RBAC, AWS KMS and secrets with Vault and Secrets Manager.
Product Engineer
Built FHIR-based healthcare data pipelines on AWS for exchanging records between healthcare systems.
- Built a data lineage and integration setup with Apache Atlas, dbt and OpenMetadata, which cut compliance audit time from three days to ten hours.
- Secured service-to-service traffic with TLS and scoped IAM policies.
Data Scientist
Did exploratory analysis and built automated data-quality checks.
- Ran data-analysis training for the team.
- Built customer segmentation models with the marketing team and ran A/B tests on the recommendations.
Software Engineer / Data Scientist
Built data-collection and reporting tools used across departments.
- Built REST APIs in Flask for the mobile and web apps.
- Built a recommendation engine that matched investment options to a user’s risk profile.
Research and publications
Peer-reviewed publications, preprints, and book chapters in healthcare, genomics, and clinical AI. Full record on ORCID.
For a research-focused overview, see my research page.
Peer-reviewed articles
- Dere, I. G., Ahmad, G. B. M., Oduleye, O. O., Ojekunle, J. A., & Sulaiman, M. A. (2023). Assessment of Operational Performance of Inland Water Transport in Borgu Local Government Area of Niger State. Journal of Environmental Studies, 5(1), 213–230.link
- Usman, A., Sulaiman, M. A., & Abubakar, I. (2019). Trend of neonatal mortality in Nigeria from 1990 to 2017 using time series analysis. Journal of Applied Sciences & Environmental Management, 23(5), 865.link
Preprints
- Sulaiman, M. A., & Oyeyemi, B. F. (2026). A Curated Pharmacogenomic Allele Catalog for Sub-Saharan African Populations. medRxiv. DOI: 10.64898/2026.08.25.26361354.link
- Sulaiman, M. A., & Oyeyemi, B. F. (2026). Concordance of Automated ACMG Variant Classification with Expert-Curated Assertions: A Systematic Evaluation Using the ClinGen Evidence Repository. Research Square. DOI: 10.21203/rs.3.rs-10615801/v1.linkcode
- Sulaiman, M. A., Oyeyemi, B. F., & Sarafadeen, H. (2026). Architectural Safety Mechanisms for Multi-Agent Clinical LLM Systems Under Knowledge Base Distribution Shift. medRxiv. DOI: 10.64898/2026.07.31.26359439.link
Book chapters
- Oyeyemi, B. F., Sulaiman, M. A., Dauda, S. O., Oyewusi, H. A., Oladipo, O. O., & Adekilekun, H. A. (2026). From Gene Discovery to Clinical Applications: The Journey of Genomic Research. In Genomics and Precision Healthcare: Innovations, Applications and Challenges, pp. 39–66.link
- Oyeyemi, B. F., Sulaiman, M. A., Dauda, S. O., Oyewusi, H. A., Oladipo, O. O., & Adekilekun, H. A. (2026). From Targeted Quantification to Untargeted Metabolomics: Applications and Prospects in Clinical Diagnosis. In Computational Systems Biology for New Chemical Entities Bioprospection, 1st ed., pp. 22–41. CRC Press.link
Dataset
- Sulaiman, M. A., & Oyeyemi, B. F. (2026). AfriPharmaGen Catalog: A Curated Pharmacogenomic Dataset for Sub-Saharan African Populations. Zenodo. DOI: 10.5281/zenodo.21910084.link
Conference presentations
- Adekanmbi, O., Sulaiman, M. A., Olomu, G., Ezule, P., & Ahmed, O. (2025). Scenario Planning for Next Best Action in Edo State. DHIS2 Annual Conference, University of Oslo, Norway.
- Oyeyemi, B. F., & Sulaiman, M. A. (2025). Elucidating the Epigenetic Landscape of Breast Cancer: Insights from Gene Expression Profiling of GDS662 Dataset. Biotechnology Society of Nigeria 37th Annual International Conference.
Projects
Engineering and research work. Case studies explain how each one works.
vartriage: clinical variant interpretation
A Python library that turns whole-genome and gene-panel VCF files into ranked candidate variants and ACMG/AMP-classified reports. Streams files with 4M+ variants in under 2 GB of memory, with trio, cohort, structural, and mitochondrial analysis. Published on PyPI.
AfriPharmaGen: pharmacogenomic interpretation for African populations
An agentic AI system for patient-level pharmacogenomic interpretation in African populations, where CPIC/DPWG guidelines are absent or miscalibrated. It calls star alleles with African-aware haplotypes, reasons through polypharmacy interaction chains (TB/HIV/malaria), and generates research reports with evidence chains and stated uncertainty for novel variants.
AfriPharmaGen Catalog
Curated pharmacogenomic data and reproducible analyses for sub-Saharan African populations, an underrepresented group in reference genomics databases. Published as a dataset on Zenodo.
Bayesian PGx allele-frequency estimation
Bayesian hierarchical allele-frequency estimation for ACMG BA1/BS1 variant classification in populations underrepresented in gnomAD, giving calibrated frequencies with uncertainty where sample sizes are small.
AARIS: on-device review of academic manuscripts
A privacy-first multi-agent platform that reviews academic manuscripts on-device, so papers never leave the machine. Parallel specialist agents (methodology, literature, clarity, ethics) run over a LangGraph workflow using Ollama for local inference, with optional external-model support kept for backward compatibility, plus MongoDB vector search and role-based access.
Multi-provider LLM service with failover
An async content service that routes each request to a suitable model, fails over between LLM and media providers when one is down, grounds answers in each workspace’s own content, and screens input and output for safety.
Multi-tenant agentic support platform
A multi-tenant conversational AI platform built as a Python microservice fleet behind a FastAPI gateway. A runtime routes conversations to configurable agents, retrieves per-tenant knowledge from a Qdrant vector store, and screens inputs through a parallel perception and safety pipeline. Background and proactive jobs run on Celery, with durable Temporal workflows for escalation, human-in-the-loop approval, and GDPR erasure. Tenant policy is enforced through OPA, voice is served over LiveKit, and observability runs on Prometheus, OpenTelemetry, and Grafana.
Agentic tech-stack recommender
An LLM assistant that recommends context-aware technology stacks through staged, natural-language diagnostic conversations. Built on a LangGraph agent with retrieval grounded in a FAISS knowledge base and FastAPI, with checkpointed conversation state and persisted logs.
End-to-end fraud detection pipeline
A full ML pipeline for financial fraud detection, from feature engineering and model training to deployment and monitoring, with anomalous-transaction scoring for behavioral risk.
Ovarian cancer prediction (M.Sc. research)
Trained and benchmarked baseline classifiers against ensemble methods on a clinical registry of 349 patients, using mRMR feature selection to cut 49 predictors down to 20 biomarkers. Compared the model families with cross-validation across three train/test split ratios, tracked in MLflow.
Oneka: master patient index
A Master Patient Index for healthcare systems that assign their own patient IDs. Oneka normalizes incoming demographics, runs probabilistic record linkage across connected systems, and maintains one canonical identity per patient that hospitals, clinics, and insurers can reference.
VMS: vulnerability management platform
A multi-tenant vulnerability management platform built as a TypeScript microservices monorepo. Separate services handle asset scanning, finding correlation, risk scoring, policy enforcement, and remediation tracking, behind an API gateway with Keycloak auth, role-based access, and PostgreSQL storage.
Digital public infrastructure sandbox
A cloud-native microservices sandbox that lets developers prototype against Digital Public Infrastructure rails, identity verification, messaging, voice, and an AI content service, behind a shared API gateway with OAuth2/JWT auth. Packaged for Docker Compose, Kubernetes via Helm, and Terraform, with Prometheus and Grafana monitoring.
Infrastructure automation with Terraform, Ansible and Jenkins
Automated cloud provisioning and application deployment on AWS using Terraform, Ansible, and Jenkins for repeatable, version-controlled infrastructure.
Writing
Articles on building multi-agent systems and on testing.
System Design for Agentic AI Projects: What to Do, What Not to Do, and When
Read articleBeyond Bugs: An Engineering Guide to Strategic Application Testing with Python
Read articleMulti-tenancy != Distributed System: The Common Pitfall of AI and Software Engineering Practices
Read articleEngineering a Multi-Agent AI Platform — Part 1: Adaptive Depth
Read articleEngineering a Multi-Agent AI Platform — Part 2: The Bandit
Read articleEngineering a Multi-Agent AI Platform — Part 3: The Feedback Loop
Read articleEngineering a Multi-Agent AI Platform — Part 4: The Night Dreaming Engine
Read articleEngineering a Multi-Agent AI Platform — Part 5: The Perception Layer
Read articleEngineering a Multi-Agent AI Platform — Part 6: Memory Under Token Constraints
Read articleEngineering a Multi-Agent AI Platform — Part 7: Circuit Breakers for LLM Systems
Read articleEngineering a Multi-Agent AI Platform — Part 8: Measuring What Matters
Read articleEducation
Ph.D. in Statistics (in view)
University of Ilorin
Research focus: computational genomics, statistical modeling, and clinical AI.
M.Sc. in Statistics
University of Ilorin
Thesis: Ovarian Cancer Prediction Using Machine Learning Algorithms.
B.Tech. in Mathematics (Statistics Option)
Federal University of Technology, Minna
Thesis: Time Series Analysis of Neonatal Mortality in Nigeria, 1990–2017.
Contact
Open to contract engineering work and to PhD positions in computational biology.
Email is the quickest way to reach me. The form below sends to the same inbox.