Architecture, delivery, observability, reliability, scaling, and operational AI in production
I design and ship production systems that connect platform infrastructure, data, and operational AI. The work covers how software is built, deployed, observed, kept reliable, and scaled without losing control of cost or change.
Rusha
Platform Engineering
System design, release workflows, environment boundaries, and operational guardrails
Rusha turns platform architecture into a repeatable delivery system, with GitOps workflows, starter templates, base images, event-driven boundaries, VCS integration, and shared controls that make changes easier to ship and safer to operate.
TierraViva AI
Data Platform Engineering
Ingest, transform, serve, observe, and scale on AWS and Kubernetes
TierraViva AI treats data infrastructure as a production operating layer, connecting ingestion, orchestration, compute, metadata, analytics engines, and serving paths so research and reporting systems stay visible, reliable, and cost-aware as workloads grow.
Agent Systems
Agent Systems
Orchestration, gateways, approvals, monitoring, and safe execution
I build agent systems as operating software, with agentic frameworks, gateways, approval paths, tool integrations, and monitoring around live workflows. The goal is safe execution, clear visibility, and deployment patterns that travel well across production environments.
Decision surfaces
Applied Analytics
Dashboards, assistants, APIs, and reporting surfaces tied to live systems
This is where the underlying platform becomes usable: dashboards for operators, APIs for products, assistants for exploration, and reporting surfaces that turn reliable data and AI systems into decisions people can act on.
In production
What holds up
7+ years building systems that ship safely, stay visible, and scale
60–80% networking cost reduction on OWA infrastructure · Elastic Dremio executor scaling without giving up query performance · Production agent systems running behind approval and observability layers · 4 portfolio systems spanning platform, analytics, and AI · One World Analytics: Senior Software Engineer & Platform Architect · The Jitu: Software Engineer III (Architecture Ownership & DevOps) · Touch Inspiration: Backend Developer
Emmanuel K. Davidson — Platform Systems, Data Platforms, and Agent Systems
About
Emmanuel K. Davidson — Platform Engineering Leader and Cloud Architect focused on production systems across platform infrastructure, data platforms, and agent operations. I build cloud-native systems that connect delivery, analytics, observability, and operational AI, with a focus on reliability, cost control, and business outcomes.. Based in Global. Contact: kipronofb@gmail.com.
Platform Engineering Leader and Cloud & Data Architect working across four domains: Platform Engineering, Data Platform Engineering, Agent Systems, and Applied Analytics. I build and run production systems that connect Kubernetes and GitOps delivery, analytics stacks, agent operations, and applied AI, turning infrastructure and data capability into reliable products and measurable outcomes.
Work Experience
One World Analytics
Senior Software Engineer & Platform Architect
Rusha Platform Architecture — Lead architect for Rusha, a production-grade deployment platform: designed NestJS API, React UI, and three microservices (API, build-service, deploy-service) with GitOps-driven deployments via ArgoCD and real-time gRPC/WebSocket log streaming.
Multi-Tenant Kubernetes Infrastructure — Implemented multi-tenant namespace isolation with project-level shared stateful services, Sealed Secrets for secret management, Cognito auth with automatic token refresh, and HMAC-verified webhook pipelines for GitHub App integration.
TierraViva AI Platform — Built a biodiversity intelligence platform that ingests and mines policy, biodiversity, patent, and research data, processes it on cloud-native analytics infrastructure, and serves the results through reporting interfaces and agent-ready workflows.
Dremio Elastic Auto-Scaling — Designed and implemented two-tier executor auto-scaling (small/large) using KEDA, Kubernetes StatefulSets, and a custom metrics exporter with graceful scale-down, reducing infrastructure costs while maintaining query performance.
LLM Automation Platform — Built production agent systems using agent runtimes and orchestration frameworks, combining LLM reasoning with live data pipelines, Kubernetes-native scheduling, Slack approval flows, and RBAC-gated operations.
MLOps Enablement — Partnered with data scientists to productionize ML models, supporting training, deployment, monitoring, and lifecycle management on Databricks and custom AI platforms.
Full DevOps Ownership — Implemented CI/CD pipelines (GitHub Actions), infrastructure automation (Terraform, Ansible), container registry management (GHCR), production observability (Grafana, Prometheus, Jaeger, OpenTelemetry), and zero-downtime GitOps deployments.
Business Intelligence & Stakeholder Reporting — Delivered executive dashboards and data visualizations supporting strategic decision-making across engineering and business leadership.
The Jitu
Software Engineer III (Architecture Ownership & DevOps)
Enterprise Data Reliability Platform — Architected and operationalized data quality pipelines using AWS Glue and Great Expectations, improving reliability across Chick-fil-A MLOps workflows and reducing data incidents by standardizing validation frameworks.
Architecture Ownership — Led design and implementation of data platforms and backend services, owning system architecture, technical direction, and cross-team alignment.
DevOps & Operational Excellence — Built CI/CD pipelines, automated deployments, and production monitoring while operationalizing data quality pipelines for enterprise-grade reliability.
Cloud-Native Systems — Developed and maintained Azure-based customer feedback platforms with Power BI integration for real-time analytics and executive reporting.
Digital Transformation — Enabled data-driven decision-making across organizations through automated reporting, dashboard delivery, and BI integration with stakeholder-facing tools.
Touch Inspiration
Backend Developer
Fintech Loan Processing Platform — Built a backend loan application and processing engine for SME financing, handling multi-stage approval workflows, risk scoring integration, and regulatory compliance.
Legal Tech Platform — Developed backend systems for a patent management platform streamlining application workflows, document generation, and status tracking.
DevOps Automation — Improved CI/CD workflows, release automation, and infrastructure monitoring for production systems using Ansible, Terraform, and cloud platforms.
Projects
Rusha
Category: Platform
Production platform system for building, validating, deploying, and operating software on Kubernetes. Combines GitOps-driven delivery, event-driven application architecture, multi-tenant namespace isolation, in-cluster builds, deployment templates, shared operational guardrails, and VCS integration across API, UI, and service layers. Serves the Platform Engineering domain.
Production agent systems spanning multi-agent orchestration, domain-specific research agents, runtime-backed workflows, profile distributions, and model-gateway infrastructure. The work includes Kubernetes-based deployments, NATS-backed specialist routing, OpenAI-compatible and Ollama-facing gateway layers, approval paths, observability, and domain workflows for biodiversity, policy, and analytical operations, with Hermes used as one runtime within that stack. Serves the Agent Systems domain.
Biodiversity intelligence platform that ingests and mines policy, biodiversity, patent, and research data, processes it through GitOps-managed cloud data infrastructure, serves it through APIs and web interfaces, and makes it available to agents for analysis, navigation, reporting, and decision support. Its implementation spans document and research corpora, ETL and orchestration systems, Spark and analytics infrastructure, serving layers, and agent-consumable knowledge workflows. Serves the Data Platform Engineering and Applied Analytics domains.
This portfolio — a shader-first cinematic 3D experience built with React Three Fiber, custom GLSL shaders, and CPU-driven physics. Features a Tech DNA neural-network hero derived from real project data, kinetic typography, and an immersive world of project zones. No GPU compute required: runs on any integrated-graphics device at 60fps desktop / 30fps mobile.
My work breaks down into four domains of expertise. Each domain has a curated set of tools, and each tool has a specific role inside that domain. Fullstack application engineering shows up inside every domain as the delivery vehicle for the UIs, dashboards, and APIs that domain depends on.
Platform Engineering
Platform systems that make software easier to ship and safer to run — service boundaries, release workflows, deployment controls, environment isolation, and operational guardrails, with fullstack delivery as the vehicle for the platform's own APIs, UIs, and log-streaming surfaces.
The layer that carries analytics from ingestion to delivery — orchestration, compute, metadata, warehouse layers, observability, and scaling controls working together as one production system, with fullstack delivery for the analytics APIs, dashboards, and serving surfaces.
Apache Airflow: pipeline orchestration
dbt: data modeling and transformation
Apache Spark: distributed compute and processing
Delta Lake: transactional storage and warehouse layer
Agent systems as production infrastructure that can be deployed, observed, governed, and improved — multi-agent orchestration, model gateways, runtime packaging, control surfaces, approval paths, observability, and domain-specific workflows, with fullstack delivery for the agent control surfaces and dashboards.
Hermes: agentic runtime framework
NATS JetStream: agent state and specialist routing
LLM (OpenAI, Ollama): model gateway and reasoning core
Where the underlying platform becomes usable — dashboards for operators, APIs for products, assistants for exploration, and reporting surfaces that turn reliable data and AI systems into decisions people can act on, built with fullstack delivery.
AWS Certified Solutions Architect (Associate) — Amazon Web Services ().Professional certification for designing distributed systems and cloud-native architectures on AWS.
Google Data Analytics Professional Certificate — Google ().Professional certificate covering the full data analysis lifecycle — from data cleaning and visualization to stakeholder reporting.
KCNA: Kubernetes and Cloud Native Associate — The Linux Foundation ().Foundational certification covering Kubernetes architecture, the cloud-native landscape (storage, networking, GitOps, service mesh), and cloud-native security principles. Certificate ID: LF-urra1y7iek. Expires: May 2028. Verify on Credly.
Education
University of Nairobi
Post Graduate Diploma in Project Planning & Management
Specialized in agile project management methodologies
University of Eldoret
BSc in Biochemistry
Graduated with honors
Publications & Thought Leadership
Understanding Kubernetes StatefulSets — Practical guide to stateful workload patterns, pod identity, and storage semantics in production Kubernetes deployments.
Cloud Cost Optimization Strategies — Techniques for reducing cloud spend through right-sizing, auto-scaling, and reserved instance planning across AWS and GCP.
Deploying AI-Powered Research Agents — Architecture and deployment patterns for LLM-integrated automation systems on Kubernetes with RBAC and approval workflows.