Senior Machine Learning Engineer

Location: Bristol, United Kingdom (Hybrid – remote + on‑site)

Pay: up to £76 p/h outside IR35

Clearance: SC Clearance required

Key skills: Python, OpenCV, object detection, MLOps, TensorFlow, PyTorch, SQL, ETL, OCR.

We are looking for a Senior Machine Learning Engineer to take ownership of machine learning solutions supporting secure, high‑integrity systems and services. This is an exciting opportunity to design, develop, deploy, and improve advanced ML models that enable data‑driven decision‑making across complex and mission‑critical environments.

• 
  
• Design, build, and optimise machine learning models across areas such as computer vision, NLP, and predictive analytics.
  
• Own the ML lifecycle from data preparation and training through to evaluation, deployment, and optimisation.
  
• Implement and maintain MLOps workflows to support continuous integration and delivery of ML models.
  
• Work closely with Data Engineers and DevOps teams to ensure production readiness and scalability.
  
• Contribute to architecture decisions for ML pipelines, model deployment, and data flows.
  
• Apply secure coding and configuration practices in line with compliance and quality standards.
  
• Mentor junior engineers and share best practice across the team.
  
• Research and evaluate emerging ML tools, techniques, and approaches to support innovation and continuous improvement.
  

• 
  
• Proven experience developing and deploying machine learning models in production environments.
  
• Extensive experience developing in Python.
  
• Strong hands‑on experience with OpenCV and object detection models including YOLO, RCNN, and Vision models.
  
• Strong understanding of object detection concepts.
  
• Experience in video analysis, including optical flow and object tracking.
  
• Knowledge of OCR models, including fine‑tuning using custom datasets.
  
• Understanding of model evaluation metrics such as Character Error Rate (CER) and Word Error Rate (WER).
  
• Experience with ML frameworks such as TensorFlow and PyTorch.
  
• Good understanding of ML architectures, hyperparameter tuning, and performance optimisation.
  
• Experience with MLOps tooling and CI/CD pipelines.
  
• Familiarity with data engineering concepts, including ETL, SQL, and data pipelines.