Nyasha M. — AI Systems EngineerDownload CV
AI SYSTEMS ENGINEER

NYASHA M.

I build production-grade AI systems, real-time data platforms, and intelligent software.

I work across AI, data engineering, backend systems and MLOps — turning models and ideas into reliable systems that operate in the real world.

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Building production AI, data and intelligent systems.

system.architectureCONCEPT / 01
INPUT SOURCESEVENT-DRIVEN SYSTEM
EventsDocumentsAPIs
01 / INGESTKafka
Event streaming
02 / DATA PLATFORMPostgreSQLPersist. Transform. Retrieve.
02 / INTELLIGENCEPython / AI & MLEvaluate. Infer. Explain.
03 / SERVEFastAPI
Reliable interfaces
ApplicationsProducts · Tools · Workflows
BUILT TO OPERATEDockerAWSObservability
From data to intelligenceBuilt as a system.
AI SYSTEMSDATA ENGINEERINGBACKENDMLOPSDISTRIBUTED SYSTEMS
Oracle Certified Java SE 17 DeveloperAWS Certified Developer – AssociateISC² Certified in Cybersecurity
01SELECTED SYSTEMS

Featured Engineering

Systems I design to explore how modern AI, data and backend infrastructure work together.

Explore all work
01AIEngineering project

SentinelRisk

Real-Time Explainable Risk Intelligence

Event-driven risk scoring with explainable predictions and an observable model lifecycle.

  • Kafka
  • Python
  • FastAPI
  • PostgreSQL
  • MLflow
  • Docker
  • Explainable AI
View case study Architecture
Architecture overview
KafkaEVENT STREAM
Risk modelEXPLAINABLE ML
FastAPIDECISION API
Event-driven · explainable
02DataEngineering project

FinStream

Modern Batch + Streaming Data Platform

A unified path from raw events to trustworthy, analytics-ready data.

  • Kafka
  • Spark
  • Airflow
  • dbt
  • PostgreSQL
  • Docker
View case study Architecture
Architecture overview
KafkaINGESTION
SparkPROCESSING
dbtANALYTICS
Batch + streaming
03AIEngineering project

PolicyLens

Enterprise Document Intelligence

Citation-first retrieval with access control, evaluation and human approval.

  • LLMs
  • RAG
  • Vector Search
  • Python
  • FastAPI
  • RBAC
  • Evaluation
  • Docker
View case study Architecture
Architecture overview
DocumentsKNOWLEDGE
RetrievalVECTOR SEARCH
LLMCITED ANSWERS
Citation-first · governed
02ENGINEERING CAPABILITIES

What I Build

Across the stack. Across the system lifecycle.

AI Systems

Intelligence that is grounded, explainable and evaluated for real-world use.

  • LLMs
  • RAG
  • Machine Learning
  • Explainable AI
  • AI Agents
  • Model Evaluation
  • AI Integration

Data Platforms

Reliable data foundations, from the first event to an analytics-ready model.

  • Kafka
  • Spark
  • PostgreSQL
  • Airflow
  • dbt
  • ETL / ELT
  • Streaming
  • Data Quality

Backend Systems

Well-defined services and integrations that hold up under real demands.

  • Python
  • FastAPI
  • Java
  • Spring Boot
  • REST APIs
  • Microservices
  • Distributed Systems

Production & MLOps

The delivery, monitoring and operational discipline that keeps systems useful.

  • Docker
  • CI/CD
  • MLflow
  • AWS
  • Linux
  • Monitoring
  • Model Deployment
  • GitHub Actions
03IN PRACTICE

Engineering Experience

Building where reliability, security and business impact matter.

View full experience

AI & Enterprise Data Specialist

AFC Commercial Bank
January 2025 — Present

AI, real-time data and intelligent automation in a banking environment.

Systems Developer

AFC Commercial Bank
September 2023 — December 2024

Backend development and integration across core banking and enterprise systems.

Software Developer

ZARNet
November 2021 — August 2023

Software development across enterprise applications, automation and integrations.

04THE BIGGER PICTURE

How I Think About AI Systems

Models are only one component of a production AI system.

SYSTEM ARCHITECTURESELECT A LAYER

Every layer matters. Every boundary is a design decision.

03 / AI / ML

A model is one part of the system.

Useful intelligence needs relevant context, measured quality and an appropriate level of control. Retrieval, evaluation and explainability are engineering concerns from the beginning.

ENGINEERING PRINCIPLE

Evaluate the complete workflow, not just the model.

LLMsRAGMachine learningEvaluation
05ENGINEERING OUTCOMES

Selected Impact

From my work in banking and enterprise technology.

12%

Fewer fraud-detection false positives

Fraud-detection work at AFC Commercial Bank, in the AI & Enterprise Data Specialist role.

100%

Workflow traceability

Traceability and auditability for workflow automation created at AFC Commercial Bank.

Real-time

Transaction data pipelines

Transaction and customer-flow pipelines using Kafka, PostgreSQL and Python at AFC Commercial Bank.

Enterprise

AI + data systems

ML and LLM deployment, sentiment analysis and enterprise AI work across banking technology teams.

06FOUNDATIONS

Certifications

Oracle

Java SE 17 Developer

AWS

Developer — Associate

ISC²

Certified in Cybersecurity

Education

MSc Computer Science

University of East London · Distinction

BCom (Hons) Information Systems

Great Zimbabwe University · Upper Second Division

Recognition

UNESCO India-Africa Hackathon

Gold Medalist

Presidential Innovation Award

Recipient

07IDEAS & OBSERVATIONS

Engineering Notes

On building, deploying and understanding intelligent systems. First notes are in the works.

Explore writing
01

Building Real-Time AI Systems with Kafka and FastAPI

Planned note · AI Systems
02

What Production RAG Actually Requires

Planned note · LLM Systems
03

Designing Explainable AI for Financial Systems

Planned note · Machine Learning
08BEHIND THE SYSTEMS

An engineer,
across disciplines.

I’m Nyasha Mandizvidza, an AI Systems Engineer focused on building production-grade AI, data and backend systems. My work sits at the intersection of software engineering, machine learning and enterprise infrastructure.

More about me
START A CONVERSATION

Let’s build
intelligent systems.

I’m interested in engineering opportunities and collaborations involving production AI, data platforms, backend systems and applied machine learning.

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