ML Engineer

Requirements:

• Genuine core ML fundamentals that you can explain, from first principles: the bias-variance trade-off, why a model is overfitting and how you'd know, what a given loss function optimises, how regularisation works, and why your chosen validation scheme is sound for this data.
• A track record of models you actually built and evaluated with the architecture, the data, the metric, and the before/after numbers.
• Fluency in Python and the modern ML stack: scikit-learn, and at least one of PyTorch / TensorFlow used for real training (not just inference).
• Solid SQL and data-wrangling (pandas / PySpark or equivalent) for large datasets.
• Sound experimental discipline: appropriate cross-validation, leakage prevention, metric selection, and the judgment to prefer a simpler model when it wins.
• The instinct to quantify. You reach for numbers by default, and you know the difference between a self-reported metric and an externally validated one.
• Strong communication and intellectual honesty: you can defend a modelling decision and, just as importantly, admit when a result doesn't hold up.

Good-to-haves:

• Deep-learning depth: architectures built or meaningfully modified from scratch, training-dynamics debugging, or published/research work.
• Probabilistic and Bayesian modelling; time-series beyond the standard toolkit (state-space, hierarchical, foundation models for forecasting).
• Genuine fine-tuning experience (LoRA / PEFT / full) with a clear eval story and the ability to say precisely what it accomplished over prompting.
• Production MLOps maturity: CI/CD for models, drift detection (PSI / Evidently), automated retraining, canary releases, and observability.
• Applied GenAI / RAG experience welcome as a complement to modelling depth, not a substitute for it.
• Kaggle, open-source ML contributions, or peer-reviewed publications.
• Domain experience in the respective domain, such as forecasting, fraud/risk, healthcare, retail, manufacturing, etc.