6 of 6 Contract TensorFlow Jobs

hands-on experience productionising models and building scalable ML systems. Machine Learning Engineer, key skills: Strong experience in Python (e.g. pandas, scikit-learn, NumPy, TensorFlow/PyTorch) Proven experience in model productionisation and deployment in real-world environments Hands-on experience with MLOps, including CI/ ...

transferable Proven experience as a Machine Learning Engineer within central government or regulated environments Strong programming skills in Python (e.g. Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch) Experience building and deploying ML models into production environments Strong knowledge of cloud platforms, particularly: Azure (ML Studio, Azure ML, Data Factory, Synapse ...

MLflow, Azure ML, Azure DevOps, or similar tools. Strong programming skills in Python and familiarity with common ML/AI libraries, including Scikit-learn, TensorFlow, and Keras. Experience implementing machine learning and large language models, including deployment, monitoring, and retraining. Familiarity with software engineering guidelines, including version control ...

/ML/LLM-based applications is desirable. Beneficial technical skills include: AWS – including certification Terraform Kubernetes Data science/ML tooling – scikit, TensorFlow, hugging face, pytorch Any ML/Gen AI tooling such as Langchain, Langsmith, ML Flow, Dataiku, Data Robot, Sagemaker, Bedrock, Weights and Biases. Oauth/ ...

training, inference, scientific workloads) Provision and optimize GPU/TPU-based infrastructure on GCP (A3/A4, TPU pods) Analyze performance across frameworks (PyTorch, TensorFlow, JAX, CUDA, ROCm) Identify system bottlenecks (compute, memory, network, I/O) Build automation tools for benchmarking and reporting Collaborate with teams to align ...

systems . (Note: This is a system-heavy deployment role, not a research or prototyping position). The Technical Stack: Strong proficiency in TensorFlow is strictly required. Experience with Kubeflow is a massive plus. Lifecycle Ownership: Complete comfort handling the end-to-end ML lifecycle: prototyping, building robust data ...