Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, MLflow, CI/CD pipelines Bonus: Experience with wearable/sensor data, player tracking, or sports video analytics TO BE CONSIDERED Please apply directly by emailing with your CV and availability. More ❯
Build APIs and microservices for scalable AI deployment. Use AI-powered dev tools like GitHub Copilot, Cursor, and Codeium to speed up iteration. Apply MLOps/LLMOps practices with MLflow, Weights & Biases, and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data engineering. Familiarity with vector databases More ❯
fine-tuning. Proficient in Python, PyTorch or TensorFlow, and scikit-learn. Practical experience deploying ML models in production environments (cloud-native or on-prem). Familiar with data versioning, MLflow, and monitoring frameworks (e.g. Weights & Biases, Evidently). Strong communicator, capable of leading project delivery autonomously. Why Join Step into a key delivery role for a high-value client while More ❯
with security tools for SAST, DAST, SCA, and secrets management (e.g., HashiCorp Vault). MLOps & AI/ML Knowledge: Must have practical experience with MLOps tools and workflows (e.g., MLflow, Kubeflow, Seldon Core) and an understanding of the ML lifecycle. o Understanding of the unique security vulnerabilities associated with AI/ML systems. Programming & Scripting: Strong scripting skills in Python More ❯
complex business problems into actionable data science projects with clear KPIs and ROI. Technical Leadership: Mentor junior data scientists, establish best practices for code (e.g., Git), model versioning (e.g., MLflow), and reproducible research. Conduct code reviews and champion a culture of technical excellence. MLOps & Productionisation: Work with ML Engineers and DevOps teams to build robust, scalable, and automated MLOps pipelines … or computer vision. Big Data Tools: Experience with big data platforms like Spark (PySpark) for handling large-scale datasets. MLOps: Familiarity with MLOps tools and concepts (e.g., Docker, Kubernetes, MLflow, Airflow) for model deployment and lifecycle management. Financial Domain Knowledge: Direct experience with at least two of the following domains: Credit Risk Modeling, Fraud Detection, Anti-Money Laundering (AML), Know More ❯
We are a Global Recruitment specialist that provides support to the clients across EMEA, APAC, US and Canada. Role Title: Sr.Databricks Engineer (AWS) Location: Glasgow Duration: 31/12/2026 Days on site: 2-3 Rate: £402/day More ❯
