Machine Learning Engineer / MLOps Engineer (Contract)
Location: Fully Remote (UK-Based)
Contract Length: 6 Months Initial Contract
Day Rate: £500 - £550 per day
Start Date: ASAP
The Opportunity:
We're seeking an experienced Machine Learning Engineer with strong MLOps and DevOps expertise to join a high-performing engineering team delivering scalable AI and machine learning solutions.
This role is ideal for someone who enjoys operating across the full ML lifecycle, from developing and deploying models to building the cloud infrastructure, CI/CD pipelines, and operational tooling that underpin production-grade AI systems.
You'll work closely with Data Scientists, Software Engineers, Platform Engineers, and Product teams to ensure machine learning solutions are robust, scalable, secure, and maintainable.
Key Responsibilities:
* Design, build, and deploy machine learning models into production environments.
* Develop and maintain scalable ML pipelines for training, validation, deployment, monitoring, and retraining.
* Build cloud-native infrastructure to support machine learning workloads.
* Create and optimise CI/CD pipelines for machine learning and software deployments.
* Implement Infrastructure as Code (IaC) using tools such as Terraform or CloudFormation.
* Manage containerised applications and ML services using Docker and Kubernetes.
* Monitor production systems, model performance, and infrastructure reliability.
* Work with Data Scientists to productionise predictive, deep learning, and Generative AI models.
* Champion MLOps and DevOps best practices across the engineering function.
* Ensure security, governance, observability, and scalability are embedded throughout the ML lifecycle.
Required Experience:
* Proven experience as a Machine Learning Engineer, MLOps Engineer, or Platform Engineer supporting ML workloads.
* Strong Python development skills.
* Commercial experience deploying machine learning models into production.
* Hands-on experience with AWS, Azure, or GCP.
* Strong understanding of DevOps and Site Reliability Engineering (SRE) principles.
* Experience building and maintaining CI/CD pipelines using tools such as GitHub Actions, GitLab CI, Azure DevOps, or Jenkins.
* Experience with Infrastructure as Code (Terraform, CloudFormation, Pulumi, etc.).
* Strong knowledge of Docker and Kubernetes.
* Experience with monitoring and observability tools such as Prometheus, Grafana, ELK, Datadog, or OpenTelemetry.
* Familiarity with ML frameworks including PyTorch, TensorFlow, Scikit-learn, or similar.
* Experience working with distributed systems and large-scale data processing.
Desirable Experience:
* Experience with Generative AI, LLMs, RAG architectures, or AI agents.
* Experience with ML platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
* Knowledge of feature stores and model registries.
* Experience with streaming technologies such as Kafka or Kinesis.
* Exposure to FinOps and cloud cost optimisation.
* Experience operating within regulated environments.
Technology Stack:
Python | AWS | Kubernetes | Docker | Terraform | GitHub Actions | Jenkins | MLflow | Kubeflow | SageMaker | PyTorch | TensorFlow | Prometheus | Grafana | Datadog | Kafka
What's on Offer:
* Fully remote working within the UK.
* Opportunity to work on greenfield AI and machine learning initiatives.
* High-impact role with significant autonomy.
* Flexible working arrangements.
* Competitive day rate.
* Potential contract extensions based on project delivery.