lavori da Data Scientist in località Regno Unito

Job Type : contract 40 hours+ p / week. 12 months contract.

About the Role

We are looking for an experienced MLOps Engineer to bridge the gap between machine learning (ML) development and production deployment. In this role, you will design, build, and maintain scalable ML infrastructure, automate model deployment, and ensure the reliability of ML models in production environments. You will work closely with Data Scientists, ML Engineers, and DevOps teams to streamline workflows and improve the operationalization of AI / ML solutions.

Key Responsibilities

• Develop and maintain CI / CD pipelines for ML model deployment and monitoring.
• Design and implement scalable ML infrastructure using cloud services (AWS, GCP, or Azure).
• Automate model training, validation, and deployment using tools like Kubeflow, MLflow, or TensorFlow Serving.
• Implement model versioning, tracking, and governance frameworks to ensure reproducibility and compliance.
• Optimize ML workloads for performance, cost efficiency, and scalability.
• Monitor model drift, retrain pipelines, and manage data versioning to ensure continued model accuracy.
• Collaborate with Data Scientists to streamline experimentation and productionization workflows.
• Ensure security, governance, and compliance of ML models in production environments.
• Work with Kubernetes, Docker, and Terraform to manage ML infrastructure.
• Develop alerting and monitoring systems for ML models using tools like Prometheus, Grafana, or Datadog.

Requirements

• Bachelor’s or Master’s in Computer Science, Data Engineering, or a related field.
• 3+ years of experience in MLOps, DevOps, or ML Engineering.
• Proficiency in Python and / or Go.
• Experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Strong understanding of containerization (Docker) and orchestration (Kubernetes).
• Hands-on experience with cloud platforms (AWS / GCP / Azure) and serverless architectures.
• Knowledge of CI / CD tools (GitHub Actions, Jenkins, ArgoCD, or CircleCI).
• Experience with ML pipeline orchestration tools (Kubeflow, Apache Airflow, or MLflow).
• Understanding of model monitoring, logging, and performance optimization.
• Ability to collaborate with cross-functional teams.
• Strong problem-solving and analytical skills.
• Passion for automation and continuous improvement.

Nice-to-Have Skills

• Experience with feature stores (Feast, Tecton).
• Knowledge of compliance standards like GDPR, HIPAA (if applicable).

Why Join Us?

• Work on cutting-edge ML infrastructure at scale.
• Collaborate with top AI / ML experts in a fast-paced environment.