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Machine
Learning
Engineering

End-to-end ML system design — from problem framing and feature engineering to production inference pipeline deployment and MLOps workflow configuration. I build models that are accurate, interpretable, and operationally sound.

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What's included

Deliverables

  • Supervised, unsupervised & reinforcement learning model development
  • Feature engineering, data preprocessing pipelines & schema validation
  • Model evaluation, cross-validation & hyperparameter optimisation (Optuna, Ray Tune)
  • Natural Language Processing (NLP): classification, entity recognition, embeddings, RAG
  • Computer vision: object detection, segmentation & image classification pipelines
  • RESTful inference API development & containerised model serving (FastAPI, TorchServe)
  • MLOps workflow: experiment tracking, model versioning & drift monitoring (MLflow, DVC)
Why choose this service

The Difference

Most ML projects stall between experimentation and production. A model that scores well in a notebook is not the same as a model that serves reliably at scale, degrades gracefully under distribution shift, and can be retrained without breaking downstream systems.

I close that gap by treating deployability as a first-class requirement — every model is engineered with versioning, monitoring, and rollback in mind from the first training run. The result is an ML system your team can operate, trust, and iterate on without continuous intervention.

Engineering Process

01
Problem Framing
02
Data Preparation
03
Modelling & Evaluation
04
Optimisation
05
Deployment
06
Monitoring & Retraining
Technology Stack

"Python • PyTorch • TensorFlow • scikit-learn • Hugging Face Transformers • LangChain • FastAPI • MLflow • DVC • Optuna • Docker • Kubernetes • AWS SageMaker • GCP Vertex AI"

The Philosophy

"Machine learning is only as valuable as the decisions it enables. I build models that are not just accurate, but interpretable, maintainable, and aligned to real business outcomes — systems that earn operational trust."

Ready to build?

Let's frame your ML problem clearly — what signal exists in your data, what decision it needs to support, and what a production-ready system looks like for your team.

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