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# Senior Machine Learning Engineer

- **Company:** Anson McCade
- **Location:** Greater London, UK
- **Experience:** Expert
- **Salary:** £84,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Big Data, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Pytorch, Large Language Models, Prompt Engineering, Apache Spark, Generative AI, Cloudformation, Containerization, Scikit Learn, Kubernetes, Xgboost, Dask, Machine Learning Operations, Terraform, Software Version Control, Docker
- **Published:** September 10, 2026
- **Apply:** https://www.collegerecruiter.com/job/2840613718-senior-machine-learning-engineer

## About the Role

Are you passionate about building impactful AI solutions and pushing the boundaries of machine learning? Do you want your work to deliver real-world value across critical national infrastructure?, * 4-5 years of hands-on ML experience.

 * Experience deploying ML models in Python using scikit-learn, XGBoost, PyTorch, or TensorFlow.
 * Experience with AWS ML services (SageMaker, Lambda, S3) in production environments.
 * Proof of experiment design, hypothesis testing, and statistical evaluation.
 * Proven ability to transition models from experimentation to production with governance and quality controls.
 * Familiarity with MLOps tooling such as MLflow, Weights & Biases, or DVC.
 * Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures.
 * Excellent communication skills, able to convey complex findings to technical and non-technical audiences., * Advanced LLM techniques: agents, tool use, and agentic workflows.
 * Knowledge of vector databases (Pinecone, Weaviate, pgvector).
 * Experience with feature stores (Feast, AWS Feature Store).
 * Containerisation and orchestration (Docker, Kubernetes, ECS).
 * Infrastructure as Code (Terraform, CloudFormation).
 * Large-scale data processing frameworks (Spark, Dask).
 * Experience in regulated industries or handling sensitive data.

## Description

* Design, develop, and iterate ML models for traditional tasks (forecasting, classification, anomaly detection) and GenAI/LLM applications.
 * Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly.
 * Transition validated experiments into production-ready solutions, collaborating with engineers and stakeholders.
 * Build and optimise ML pipelines using AWS services and experiment tracking tools.
 * Implement robust experiment tracking, model versioning, and reproducibility practices.
 * Support production models through monitoring, performance analysis, and continuous improvement.
 * Apply responsible AI practices, including model explainability and fairness assessment.
 * Mentor junior colleagues and share learnings across the team.


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