★ Google GenAI Accelerator AwardAga Khan UniversityAWSAzureGCPStanford HAILJMU · EPCC
I lead the data infrastructure, AIOps, and cloud engineering function at Aga Khan University, with 12 years building and running production data platforms across AWS, Azure, and GCP for distributed teams in Kenya and Pakistan. My MedGemma-based clinical decision support work, grounded in Kenyan Ministry of Health guidelines, was recognized through the Google GenAI Accelerator Award.
I build the data foundation that makes AI and analytics trustworthy, scalable, and production-ready.
My platform work sits where data engineering meets AI. I have built feature-engineering pipelines that cut model-ready dataset preparation time by 30%, put statistical anomaly detection in front of data quality problems before users found them, and designed the data infrastructure under LLM and automation workloads.
Taking a MedGemma-based assistant from prototype to daily use by doctors at Aga Khan University Hospital — and earning the Google GenAI Accelerator Award along the way.
The lakehouse, streaming, and governance foundation behind AKU's research and hospital analytics across Kenya and Pakistan — 99.9%+ uptime with p99 freshness SLOs.
Building the data platform for e-commerce across Kenya and Uganda: multi-AZ Postgres replication, 15-minute freshness SLOs, and MLOps that cut costs 60%.
Field notes on data platforms, streaming, and applied AI in production.
Posts published on this site live under /blog/; earlier pieces link out to LinkedIn.
Projects
Case studies of production systems I have led, and a selection of open-source work across data infrastructure, healthcare AI, MLOps, and applied AI. The full repository list lives on GitHub.
2.5M+ rows/day through CDC pipelines, multi-AZ Postgres replication with <10s failover, and a recommendation engine that cut the retargeting programme’s cost by 60%.
Conformal prediction for clinical AI under covariate shift.
Gaussian Processes
Healthcare AI in low-resource African contexts.
Compute Governance
Access barriers for African AI innovators.
Work Experience
2025–present
Aga Khan University — Nairobi, Kenya
Manager, Data Infrastructure, AIOps, MLOps & Cloud Engineering
~ Own data and AI platform delivery for AKU Global Data & Innovation, reporting to the Chief Data Officer. Lead distributed engineers across Kenya and Pakistan. Set SLAs for data uptime, quality, and freshness across the AKU Hospital data platform, the NIH-funded Uzima-DS Consortium, and internal research teams.
~ Took Afya Gemma from prototype to production. The MedGemma-based clinical decision support system now operates daily for resident and intern doctors at AKU Hospital (Google GenAI Accelerator Award). Designed the two-stage retrieval architecture (Gemini 2.5 Flash classifier before MedGemma generation) on a ChromaDB vector store.
~ Architected and scaled real-time and batch data platforms supporting research, analytics, and clinical operations across Kenya, Pakistan, and external consortium partners. Designed the secure Conversational RAG platform on Azure OpenAI, PgVector, and Azure AI Search with full data sovereignty.
~ Started the early architecture and prototyping of Afya Gemma.
~ Built foundational data pipelines and platform components. Built the university's first enterprise clinical data repository (records 2008–present). Designed multi-country ingestion pipelines including real-time Fitbit streaming for 615 healthcare workers in Kenya.
Kafka · Flink · Spark · Python · Azure Data Factory · Delta Lake · Airflow
2021–2022
Copia Global — Nairobi, Kenya
Data Platform Manager (AWS)
~ Architected AWS-based data platforms with batch pipelines processing 2.5M+ rows/day for e-commerce across Kenya and Uganda, including a SageMaker recommendation engine whose targeted discounts cut the retargeting programme’s cost by 60%. Built the data engineering function from the ground up.
~ Owned the design and evolution of AWS-based data platforms supporting analytics, operations, and ML use cases. Delivered production ML systems including recommendation engines.
~ Redesigned streaming pipelines and data contracts to reduce cloud costs by ~25%. Built automated ML infrastructure on GCP and delivered architecture, implementation, and DataOps practices.