17 of 17 Permanent MLflow Jobs in London

efficient deployments using Docker, Kubernetes (AKS/EKS/GKE), and Terraform/Bicep. Lead enterprise MLOps capabilities using Azure ML, SageMaker, Vertex AI, MLflow, and Kubeflow, integrated with CI/CD (GitHub Actions, Azure DevOps, Jenkins, Argo CD). Oversee observability and monitoring using Prometheus, Grafana, ELK/ ...

statistical evaluation Proven track record transitioning models from experimentation to production with appropriate governance and quality controls Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures It Would Be Great If You Also ...

methodologies . Experience building production-grade AI systems using Python and ML frameworks (PyTorch, TensorFlow). Knowledge of MLOps/LLMOps practices and tooling (MLflow, Kubeflow, Docker, Kubernetes). Experience delivering AI solutions on AWS, Azure or GCP Experience leading or mentoring teams of AI engineers or data scientists. Robert ...

experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation ...

XGBoost, LightGBM, or CatBoost. Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe). Familiarity with ML lifecycle tools such as MLflow, Kubeflow, or Azure ML Pipelines. Experience working with distributed data processing (e.g., PySpark) and SQL. Understanding of software engineering best practices, including version control (Git). ...

scalable, maintainable, and production-ready AI systems. Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow). Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK ...

frameworks like AutoGen, CrewAI or LangGraph . Experience building RAG architectures with vector databases (Pinecone, Weaviate, FAISS). Strong understanding of MLOps/LLMOps (MLflow, Kubeflow, Docker, Kubernetes). Experience deploying AI solutions on AWS, Azure or GCP . Solid software engineering and full-stack development experience in enterprise environments. ...

teams and delivering enterprise-grade data solutions. Preferred Qualifications Databricks certifications (e.g., Databricks Certified Data Engineer Professional, Architect ). Experience building ML solutions using MLflow or integrating with cloud ML services. Experience in BFSI, Retail, Telecom, or Healthcare data environments. Experience with real-time data processing (Kafka, EventHub, Kinesis). ...

learn, XGBoost, PyTorch, or TensorFlow Strong experience with cloud-based ML deployment (e.g., AWS SageMaker, Lambda, S3) Knowledge of MLOps/LLMOps tooling (e.g., MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures Ability to absorb complex scientific and technical concepts ...

Strong experience in at least one - Azure, AWS Very Nice to Have: • Databricks certifications (Data Engineer Associate/Professional) • Experience with Feature Store/MLflow • Exposure to Databricks AI/Mosaic AI/Model Serving If you’re interested in building high-performance Databricks platforms at scale, send your ...

Knowledge of SQL and/or NoSQL databases AI/Machine Learning Exposure to ML/AI workflows or data science tooling Experience with MLflow or model tracking systems Scientific/Data Applications Experience working with scientific or bioinformatics datasets Exposure to single-cell RNA-seq, DNA/RNA sequences ...

test splits are dangerous in time series and what to do about it. MLOps & Productionisation. Experience taking models from notebook to production: experiment tracking (MLflow, W&B), model versioning, feature stores, drift detection, and retraining triggers. You can build a model that doesn't just work once - it works reliably ...

earth team What they're looking for: Solid experience with Databricks (Azure or AWS) Good understanding of tools like Delta Lake, Unity Catalog, MLflow Strong SQL and Spark (Python or Scala) Experience working with data pipelines and modern data platforms A good grasp of data modelling, warehousing, and general data ...

training frameworks (DDP, FSDP, DeepSpeed). Background in NLP tasks such as NER, semantic similarity, QA, or dialogue systems. Experience with experiment tracking platforms (MLflow, Weights & Biases). Exposure to agentic AI paradigms (ReAct, chain-of-thought reasoning, tool use). Prior experience in leading AI research labs or high ...

including Circadia’s clinical monitoring hardware, working with firmware and embedded engineering teams as needed. Manage model versioning, promotion, and rollback workflows through the MLflow model registry. Evaluate and implement strategies for safe model rollouts (e.g. shadow deployments, canary releases) as the platform matures. Experiment Tracking & Model Registry Maintain … improve the MLflow-based experiment tracking and model registry infrastructure. Establish conventions for experiment logging, artifact storage, model metadata, and lineage tracking. Enable ML engineers to move seamlessly from experimentation to production deployment with minimal friction. Data & Model Versioning Implement and maintain training data versioning and dataset management practices ...

turn ideas into impactful solutions. Optimize and deploy models with tools like TensorFlow Serving, TorchServe, ONNX, and TensorRT. Build and manage ML pipelines using MLflow, Kubeflow, and Azure ML Pipelines. Work with large-scale data using PySpark and integrate ML solutions into production environments. Monitor and improve model performance … LightGBM, CatBoost, TensorFlow, Keras, PyTorch). Familiarity with model deployment and serving tools (ONNX, TensorRT, TensorFlow Serving, TorchServe). Experience with ML lifecycle tools (MLflow, Kubeflow, Azure ML Pipelines). Knowledge of distributed data processing (PySpark) and software engineering principles (Git). A collaborative mindset and excellent problem-solving abilities. ...

This role is critical to ensuring models are robust, reproducible and production-ready. Key responsibilities: • Deploy, manage and monitor ML models in production • Own MLflow, experimentation tracking and reproducibility • Build scalable training and deployment pipelines • Implement CI/CD for ML workflows • Optimise AWS infrastructure for performance and cost • Ensure … monitoring of ML endpoints Key details: • Salary: £80,000–£110,000 + discretionary bonus • Working: Hybrid, 3 days per week in London • Stack: Python, MLflow, AWS (SageMaker), CDK, Docker • Visa: Cannot sponsor Interested? Please apply below. ...