Senior MLOps Engineer – Build & Run ML Platforms

Build the systems behind next-generation AI authentication

Our global client is building advanced behavioural intelligence technology that enables secure, adaptive digital identity. By analysing how people naturally interact with devices, their AI systems generate powerful authentication signals designed for real-world use at scale.

They’re now moving from R&D into live customer deployments and are looking for an experienced Senior MLOps Engineer to help take behavioural AI models into production and keep them running reliably as usage scales globally.

This is a hands-on, production-focused role with real ownership across how machine learning is deployed, monitored, and operated in the real world.

If your experience has mainly been configuring managed services rather than building and operating ML infrastructure yourself, this role is unlikely to be a good fit.

This is not a “managed platform only” MLOps role.

You’ll be deeply involved in building, deploying, and running ML systems yourself owning everything from training pipelines through to low-latency inference in production.

You’ll work closely with strong ML, data, and engineering teams and play a key role in shaping how models are deployed, monitored, and scaled as customers start relying on them for authentication.

• 
  
• Turning ML models into production-ready, customer-facing services
  
• Building CI/CD pipelines for models, not just application code
  
• Designing and running low-latency, high-availability inference systems
  
• Deploying models for inference using frameworks such as FastAPI, BentoML, or similar
  
• Monitoring live models for performance issues, drift, and failures
  
• Scaling ML systems as pilot and production customers onboard
  
• Building and managing infrastructure using Infrastructure as Code
  
• Helping mature MLOps practices globally as the platform grows
  

You don’t need to tick every box, but you should have real experience running ML in production and taking ownership beyond experimentation.

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• Experience in MLOps, ML Engineering, or ML-heavy DevOps roles
  
• Strong Python and hands-on ML framework experience (e.g. PyTorch, TensorFlow)
  
• Experience deploying and serving ML models in production environments
  
• Experience standing up and operating MLOps tooling yourself (e.g. deploying MLflow, Prometheus, Grafana on Kubernetes), NOT just consuming managed services
  
• Containerisation and orchestration (Docker, Kubernetes, or ECS)
  
• AWS experience (e.g. ECS, S3, SageMaker, Lambda)
  
• CI/CD for ML workflows
  
• Infrastructure as Code (Terraform, CloudFormation, etc.)
  

• 
  
• Low-latency or real-time ML systems
  
• Model observability and monitoring at scale
  
• A/B testing or canary deployments for ML models
  
• Experience with security-sensitive systems (auth, identity, fintech)
  
• Startup or scale-up environment experience
  
• Work on real-time behavioural AI used in authentication
  
• High ownership — you’ll help define how ML runs across the company
  
• Direct impact as the platform moves into live customer deployments
  
• Join at a pivotal growth stage, not once everything is already fixed