Outcomes first. Technology second.
Five practices that cover the full journey of an AI system — from an honest feasibility read to a governed, production-grade deployment. Each answers the same four questions: the business problem, the approach, the deliverables, and the impact.
Machine Learning Engineering
Predictive models that hold up in production.
The problem
Many organizations have data and prototypes, but no dependable path from a notebook to a model that business teams can actually rely on for decisions.
The approach
I design the full modelling pipeline — exploratory analysis, feature engineering, class balancing, model selection and rigorous validation — with reproducibility and evaluation built in from the first commit, not bolted on later.
Deliverables
- End-to-end training pipeline (EDA → features → balancing → tuning)
- Benchmarked model selection with documented evaluation metrics
- Containerized inference service with automated tests
- Reproducible, version-controlled codebase and hand-off documentation
Business impact
Decision-grade models — credit risk, fraud, demand and health forecasting — that are measurable, auditable and ready to deploy.
Generative AI & LLM Systems
RAG and agents that are grounded, not guesswork.
The problem
Off-the-shelf LLMs hallucinate, leak context and are hard to trust with proprietary or regulated data. Turning a demo into a dependable internal system is where most projects stall.
The approach
I build retrieval-augmented and agentic systems around your own knowledge — with grounding, evaluation, guardrails and orchestration — so answers are traceable and the system behaves predictably under real workloads.
Deliverables
- RAG architecture over private/sovereign knowledge bases
- Multi-agent orchestration and tool use (LangChain / LangGraph)
- Prompt engineering and evaluation harnesses
- Production APIs with monitoring and cost controls
Business impact
Generative AI that employees and customers can actually trust — grounded in your data, evaluated for quality, and safe to put in front of real users.
MLOps & Production Deployment
The pipeline that keeps AI alive after launch.
The problem
A model that works once is not a product. Without CI/CD, containerization, monitoring and reproducibility, AI systems silently rot — and no one notices until they fail.
The approach
I stand up the operational backbone: automated pipelines, containerized services, cloud deployment and observability, so models can be retrained, redeployed and trusted continuously.
Deliverables
- CI/CD pipelines (GitHub Actions / Azure DevOps)
- Containerization & orchestration (Docker, Kubernetes)
- Cloud deployment (AWS, Azure) incl. serverless APIs
- Experiment tracking & model registry (MLflow)
Business impact
AI systems that ship reliably, scale predictably and stay maintainable — turning one-off models into durable production assets.
AI Governance & Responsible AI
Ship AI you can defend to a regulator.
The problem
In healthcare, finance and insurance, an unexplained or non-compliant model is a liability. Teams need AI that is governed, monitored and documented — not just accurate.
The approach
I embed governance into the system: evaluation frameworks, monitoring, documentation and privacy-by-design aligned with GDPR and responsible-AI practice, so stakeholders can trust and audit what the AI does.
Deliverables
- Model evaluation & monitoring frameworks
- GDPR-aligned, privacy-by-design data handling
- Governance documentation & model cards
- Responsible-AI review of existing systems
Business impact
AI initiatives that pass internal risk, legal and compliance review — reducing regulatory exposure while accelerating approval.
AI Strategy & Advisory
From research to a roadmap you can execute.
The problem
Leaders are told AI is essential but are handed hype instead of a plan. They need an honest read on what is feasible, what it costs, and where it creates real value.
The approach
Grounded in applied mathematics and peer-reviewed research, I assess your data, use cases and constraints, then translate them into a pragmatic, prioritized roadmap — with clear build-vs-buy calls.
Deliverables
- AI opportunity & feasibility assessment
- Prioritized, costed delivery roadmap
- Architecture & build-vs-buy recommendations
- Team enablement and technical due diligence
Business impact
Confident, evidence-based AI decisions — so investment goes to the use cases that will actually reach production and pay off.
Not sure which of these you need?
Most engagements start with a short conversation to frame the problem. Bring the challenge — we'll figure out the right shape of the work together.