I design and own production-grade machine learning, analytical and backend systems that operate under real-world constraints, where reliability, traceability, and long-term performance matter.
With over 5 years of experience across Europe and Asia, I work end-to-end at the intersection of AI, software engineering, and cloud infrastructure, delivering systems that hold up beyond experimentation.
Services
I help teams design, deliver, and operate production-grade software, data, and AI systems. From first idea to long-term reliability in production.
Design and build reliable software systems and internal tools.
- Backend APIs and services designed for long-term maintainability
- Automation and internal tooling that reduce operational load
- Cloud-native systems built with reliability and observability in mind
Turn complex data into actionable insights and decision support.
- Exploratory analysis focused on actionable patterns and decision clarity
- Statistical modelling and experimentation with explicit assumptions and limits
- Decision metrics and performance analysis tied to real operational outcomes
Robust data and ML pipelines built for real operational constraints.
- Data pipelines and feature layers designed for consistency, versioning, and scale
- Training, inference, and monitoring workflows built for production constraints
- Traceability and reliability mechanisms to understand, debug, and trust models over time
End-to-end AI solutions combining ML, GenAI and software engineering.
- Predictive ML and GenAI workflows designed for controlled behavior and reliability
- Evaluation, guardrails, and monitoring to manage failure modes and drift
- Integration of AI components into existing systems and business workflows
Experience
My experience at various companies
Worked on large-scale backend systems for real-time bot and fraud detection, supporting high-volume traffic and strict reliability requirements. Owned critical components spanning behavioural signal ingestion, validation pipelines, and fraud evaluation logic, with a strong focus on correctness, performance, and operational robustness in production.
Delivered data analysis and modelling work to support decision-making on complex infrastructure and urban systems projects. Worked closely with engineering and domain experts to translate requirements into analytical outputs, focusing on clarity, traceability, and practical impact rather than purely exploratory analysis.
Designed and implemented applied machine learning and analytical components within industrial and operational contexts. Contributed to data modelling, evaluation workflows, and system integration, ensuring analytical outputs were reliable, interpretable, and aligned with real-world constraints beyond experimentation.
Built internal automation tools and supported LEAN industrialisation initiatives in a highly regulated engineering environment. Combined software development with process optimisation, contributing to cost, time and defect reduction while operating within strict documentation and coordination constraints.
Engaged in research-oriented engineering work combining software development, experimentation, and system prototyping. Collaborated with academic teams to design, build, and evaluate experimental systems, strengthening foundations in analytical thinking, modelling, and disciplined experimentation.
Developed secure software and embedded system components in a security-critical environment. Worked on authentication flows, secure communications, and system integration, gaining early exposure to reliability, security constraints, and documentation-driven engineering practices.
Built a strong engineering and analytical foundation through applied projects and technical research. Worked across software development, systems modelling, and experimental work, establishing the discipline and rigour later applied in industrial and production environments.
Skills
A systems-first skill set spanning software engineering, data, machine learning and cloud delivery.
Backend services, interfaces, performance and dependable delivery practices.
Homelab
Personal R&D for reliable software, infrastructure and applied AI
I maintain a personal experimentation environment where I can test ideas end-to-end, not only the software itself, but also how it behaves once deployed and operated. This allows me to surface risks, trade-offs, and failure modes early, before they reach users or production environments.
It’s a controlled space to validate assumptions early, understand system behaviour over time, and reduce delivery risk before changes reach real users or production environments.
- Software behaviour: Interfaces, integrations, data workflows, and how changes propagate across services.
- Infrastructure behaviour: Deployment patterns, stability under load, failure recovery, and service dependencies.
- Reliability: Failure modes, performance bottlenecks, alerting quality, and observability signals.
- Applied AI: Practical limits of models, evaluation challenges, and integration trade-offs in real systems.
This practice helps me deliver solutions that remain dependable beyond the first demo.
By testing both the product and the way it runs in real conditions, I reduce surprises, improve maintainability, and make trade-offs explicit - speed vs. cost, simplicity vs. scalability, and performance vs. robustness.
In practice, this leads to fewer surprises in production, clearer trade-offs during design, and systems that age more gracefully over time.
- What happens to response time when a new feature is introduced?
- How does a service behave when a dependency becomes slow or unavailable?
- How quickly are issues detected and diagnosed through monitoring?
- Do data or model changes silently break downstream workflows?
- How safely can a system be updated, rolled back, or recovered?
This environment complements my professional work by allowing me to validate ideas independently and continuously, outside of delivery timelines.
Business Impact
Decisions grounded in financial reality, not technology for its own sake
- AI and analytics are financial decisions, not technology experiments.
- The objective is measurable impact: lower operating cost, higher asset productivity, reduced operational and compliance risk, and stronger margin resilience.
- Under cost pressure, volatility, and talent scarcity, data-driven systems are among the few levers that improve performance without increasing headcount or capital expenditure.
These are the types of outcomes I aim for when designing and delivering the systems described above.
Let's connect
AI transforms how we work, not why we work. That stays human.
Open to discussions around applied AI, machine learning systems, and production-ready solutions.