Nicael Jooste
Digital Water Systems & Platform Engineering.
Internal Platforms · Simulation Workflows · Applied AI
I build inspectable digital-water systems: internal SDKs, typed APIs, PHREEQC/EPANET-oriented simulation workflows, decision-support tools, and AI-assisted engineering patterns.
Profile
I am strongest where the problem is not just writing code, but deciding what system should exist. My work combines water-sector process knowledge, data engineering, software delivery, and AI leverage to move from ambiguous operational needs to usable internal platforms.
I am deliberately broad across frontend, backend, data, AI, and domain modeling, but the common thread is consistent: turning physical-infrastructure knowledge into software systems that can be inspected, trusted, and maintained.
I care about systems that compound: shared SDKs, typed APIs, operational applications, simulation wrappers, documentation, validation gates, and AI-enabled workflows that improve judgment without hiding accountability. Recent work includes Python/API integration patterns for PHREEQC and EPANET-oriented scenario workflows.
Where I Work Best
I am especially interested in the missing middle between traditional IT procurement and real internal software capability: the lightweight engineering practices utilities need when problems are too domain-specific to buy whole.
Turning Operations Into Software
Converting treatment processes, telemetry, lab data, asset context, domain models, and operational assumptions into software teams can inspect and use.
Building Reusable Foundations
Building reusable Python SDKs, typed APIs, web applications, documentation standards, CI/CD practices, and shared data-access patterns.
Using AI Without Hiding Judgment
Using AI-assisted workflows to accelerate implementation while keeping architecture, validation, governance, and domain judgment explicit.
Engineering Stack & Domain Systems
The public-safe stack I want hiring managers to notice first: software interfaces, data foundations, solver-oriented workflows, validation, and water-domain context.
Decision Support & Adoption
These are supporting capabilities rather than the core engineering stack: useful in utility environments, but strongest when they sit on top of reliable data, APIs, validation, and ownership.
Operational Reporting Surfaces
Power BI and web interfaces where operational, engineering, and management audiences need the same data to become usable decision support.
Governed LLM Adoption
Early LLM experimentation, a multidisciplinary focus group, practical ChatGPT/Copilot pilots, and responsible-use guidance without overstating this as autonomous AI engineering.
Stakeholder Translation
Management briefings, roadmap framing, documentation, and delivery planning that keep internal software work aligned with operational ownership.
Capability Stories
Most of my work sits inside infrastructure organizations, where the hard part is connecting data, software, domain expertise, governance, and adoption into systems people can trust. The digital-twin work is the clearest example: a move from reporting toward an inspectable operational system, with live context, historical replay, scenarios, topology, metadata, validation logic, and controlled paths toward solver-backed analysis.
Digital-Twin Concepts For Water Operations
Turning treatment and distribution context into inspectable software: telemetry, lab data, replay, scenarios, topology, metadata, validation logic, and simulation-oriented workflows.
Internal SDKs And Data Foundations
Building shared Python foundations that sit above enterprise data platforms and give domain experts reusable interfaces for analysis, automation, and internal tools.
Governed GenAI Adoption
Helping move from curiosity to governed practice: early LLM experimentation, a multidisciplinary focus group, management briefings, responsible-use guidance, Copilot adoption, and AI-assisted engineering workflows.
Public References & Sector Work
Much of my strongest work is internal, so I use public references, generalized case studies, and transferable architecture patterns rather than proprietary screenshots or implementation details.
Field Experience
Citizen science with local inhabitants
Phetchaburi River Basin, Thailand
A local monk participates in our water level monitoring effort by taking a daily photo of the river gauge. This helped explore the feasibility of community-based data collection using the Mobile Water Management app. Fun fact: the monks kept up their daily monitoring efforts for years after our project ended!
Let's talk
I'm interested in serious conversations about digital water, internal platforms, AI-assisted engineering, and decision-support systems for infrastructure teams.