Machine Learning Engineering in the United Kingdom
Custom ML models trained on your data — vision, time-series, and tabular — packaged as production APIs with monitoring and re-training pipelines.
Who this is for
Teams who need a custom model, not an LLM wrapper, and want it to keep working in 6 months.
What problem this solves
An LLM API call costs $0.03 forever. A custom model costs $0.0001 once it's running, but requires real engineering to train, serve, monitor, and re-train. Most consultancies stop at the Jupyter notebook.
Why this matters specifically in the United Kingdom
UK clients put heavier weight on data governance, ICO compliance, and clear contractual SLAs. Engagements typically use a UK Ltd or NHS Digital procurement framework. We have shipped a production NHS clinical-decision-support platform and a UK visa-sponsorship analytics product.
What you get
- Trained model checked into MLflow with metrics, lineage, and a documented baseline
- FastAPI inference service in a Docker container, deployed to your infra
- Prediction monitoring + drift detection
- Re-training pipeline triggered on data drift or schedule
How the engagement runs
- Problem framing. Classification vs. regression vs. recommendation, success metric, baseline accuracy required.
- Data audit + labelling plan. Tell you what data you need, what's missing, and how to get there.
- Training. Baseline → tuned → ensembled. Each step logged in MLflow with experiment-level diffs.
- Production. FastAPI + Docker, A/B framework for shadow deployment, monitoring + alerts.
Deliverables
- Training pipeline (Python) checked into your repo
- Production FastAPI inference service
- MLflow experiment registry
- Monitoring dashboard (Grafana / Sentry / custom)
- Re-training pipeline (Airflow or simple cron)
Outcomes you can expect
- Production accuracy at or above the baseline you set
- Inference cost an order of magnitude below an LLM API call
- Model card documenting limits, biases, and known failure modes
Pricing in UK
Engagement size: £4,000–£50,000 GBP per engagement.
Hourly rate: £75–£150 GBP per hour.
How we contract: Engaged via UK Ltd contract, IR35 outside (we work as a substitutable supplier, not a personal-services contract), or through agency-of-record arrangements.
Timezone & availability
Operates 9am–6pm GMT/BST with strong Pakistan Standard Time overlap (PKT is GMT+5)
Tech stack
- PyTorch, Keras, TensorFlow, scikit-learn, XGBoost, LightGBM
- FastAPI, Docker, Kubernetes (when needed)
- MLflow, DVC for experiment + data versioning
- Apache Airflow for re-training
- Computer vision: YOLO, ResNet, custom CNNs
- Time-series: ARIMA, Prophet, gradient-boosted trees, neural nets
Relevant case studies
- AgenticAI - AI-Powered CV Screening Platform — An intelligent recruitment platform that uses AI to analyze and rank CVs against job requirements, helping companies find perfect candidates in minutes instead of weeks.
- Enterprise Data Pipeline & Analytics Engine — A production-grade data engineering pipeline processing 10M+ records daily with automated ETL workflows, real-time analytics, and comprehensive business intelligence reporting.
- Deep Learning Image Classification & Object Detection API — A production ML API for image classification and object detection using PyTorch and Keras, deployed with FastAPI and Docker for scalable inference.
- Statistical Analysis & Predictive Modeling Suite — A comprehensive statistical analysis platform combining Python and R for advanced analytics, predictive modeling, and automated report generation.
Questions British buyers ask about ML engineering
- How do you contract with British clients?
- Engaged via UK Ltd contract, IR35 outside (we work as a substitutable supplier, not a personal-services contract), or through agency-of-record arrangements.
- What about regulatory compliance in the United Kingdom?
- We work to UK GDPR + Data Protection Act 2018, ICO registration, NHS Digital DSP Toolkit (for healthcare work), FCA-adjacent guidance (for fintech work). Where audited compliance certifications are required, we partner with the right specialist firm and ship code that meets the technical controls.
- What's the timezone overlap?
- Operates 9am–6pm GMT/BST with strong Pakistan Standard Time overlap (PKT is GMT+5)
- What's a typical ML engineering engagement size in UK?
- £4,000–£50,000 GBP per engagement, structured against fixed milestones. Hourly engagements are billed at £75–£150 GBP per hour.
- When should I use a custom ML model vs. an LLM API call?
- Use an LLM when the task is open-ended language and your volume is low (under ~100k requests/month). Train a custom model when the task is narrow (classification, detection, ranking) and your volume justifies the upfront cost — typically beyond ~500k inferences/month, or when latency below 100ms matters.
- Do you fine-tune LLMs?
- Yes — LoRA / QLoRA fine-tuning on open-source LLMs (Llama, Mistral, Qwen) when you need a smaller, cheaper, on-prem model that knows your domain. We tell you when fine-tuning is the right call vs. RAG vs. prompting.
- How do you handle data drift?
- Two layers: (1) feature-distribution monitoring at inference time, (2) prediction-quality monitoring against a delayed-label backfill. When either crosses threshold, the re-training pipeline kicks off automatically.
- What about explainability?
- SHAP for tabular and tree-based models. Grad-CAM for vision. Model cards for everything. If the model will inform a regulated decision (medical, financial, hiring), explainability is part of the spec from day one.
- Can you work on GPU-heavy training?
- Yes. We've trained on Lambda Labs, RunPod, AWS p3/p4 instances, and on-prem GPUs. We tell you the cost up front and stop at the budget.
- What if the model doesn't hit the accuracy target?
- We agree on a stop-loss in the SOW. If after the first training round the baseline is unreachable, we pause, do a data audit, and tell you what would unblock it (more data, better labels, different architecture). You don't pay for the second round of training without your approval.