1 to 25 of 172 MLflow Jobs in the UK

BigQuery). AI/ML: proficiency in the Python ML ecosystem (scikit‐learn, XGBoost, Prophet), experience building and deploying models, and familiarity with MLflow or equivalent MLOps tooling. Generative AI: practical experience with LLM APIs (Azure OpenAI, OpenAI, or equivalent) for building intelligent interfaces or automating classification, tagging, and cost ...

DeepSpeed, Megatron-LM, or equivalent multi-node training frameworks.Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms.MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI).Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector ...

equivalent multi-node training frameworks. Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI).Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone ...

Langsmith/Langfuse, Gen AI/Agentic AI , Cloud platforms (Azure AI Foundry, AWS Bedrock/Sagemaker, GCP Vertex AI) , MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes).Preferred•Delivered AI projects within Agile frameworks•Experience on Gen AI Feedback Analysis, topic modelling, sentiment analysis•Knowledge of AgentOps and OpenTelemetry ...

Python and experience with core data science and ML libraries (e.g., scikit-learn, pandas, PyTorch or TensorFlow). Practical experience with Databricks, including MLflow for experiment tracking and model management, and Spark-based data processing. Familiarity with Generative AI concepts and tooling, including working with LLMs, prompt engineering, RAG patterns ...

level Python proficiency and experience with core data science and ML libraries (e.g., scikit-learn, pandas, PyTorch or TensorFlow).Practical experience with Databricks, including MLflow for experiment tracking and model management, and Spark-based data processing. Familiarity with Generative AI concepts and tooling, including working with LLMs, prompt engineering ...

level Python proficiency and experience with core data science and ML libraries (e.g., scikit-learn, pandas, PyTorch or TensorFlow).Practical experience with Databricks, including MLflow for experiment tracking and model management, and Spark-based data processing.Familiarity with Generative AI concepts and tooling, including working with LLMs, prompt engineering, RAG patterns ...

equivalent multi-node training frameworks. Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases ...

TensorFlow Scikit-learn XGBoost Experience deploying ML solutions on AWS, including services such as SageMaker, Lambda, and S3. Strong understanding of MLOps tooling, including MLflow, Weights & Biases, or Data Version Control (DVC). Experience developing LLM or Generative AI applications, including RAG architectures and prompt engineering. Knowledge of LLMOps frameworks ...

Python and/or Java/Scala, with clean coding, testing and code review practices. Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, or equivalent. Proven experience with Kubernetes, Docker, Helm and infrastructure as code (Terraform/CloudFormation). Cloud experience with AWS, Azure ...

approaches tooling, methodology, and model governance. Technical Environment Languages/Tools: Python (NumPy, Pandas, Scikit learn), deep learning frameworks (PyTorch/TensorFlow), SQL. MLOps: MLflow, cloud ML platforms (Azure ML, AWS SageMaker, or Databricks). Focus areas: Predictive modelling, recommender systems/personalisation, and increasingly, applied LLM/GenAI ...

statistical evaluation Proven experience moving models from experimentation into production with appropriate governance and quality controls Hands-on experience with MLOps tooling such as MLflow, Weights & Biases or Data Version Control Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation (RAG) Familiarity with LLMOps frameworks ...

Azure ML), and GCP (Vertex AI). CI/CD Tools: Experience with GitLab CI, GitHub Actions, Jenkins, or specialized MLOps platforms (e.g., Kubeflow, MLflow). Preferred Qualifications Conversational Context: Experience with dialogue management, prompt engineering, and Retrieval-Augmented Generation (RAG). Data Analytics: Familiarity with real-time data streaming ...

testing and statistical evaluationProven experience moving models from experimentation into production with appropriate governance and quality controlsHands-on experience with MLOps tooling such as MLflow, Weights & Biases or Data Version ControlPractical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation (RAG)Familiarity with LLMOps frameworks such ...

deep learning techniques (e.g., TF-IDF, word embeddings, CNNs, RNNs) Familiarity with generative AI and prompt engineering Experience with Azure Databricks, MLflow, Azure ML services, Docker, or Kubernetes Experience working in large, complex organisations or regulated industries *This role is unable to provide sponsorship ...

Science, Machine Learning, or related field Nice to Have Published research in top ML venues (NeurIPS, ICML, ACL, CVPR). Experience with MLOps tools: MLflow, Weights & Biases, DVC. Familiarity with distributed training frameworks (DeepSpeed, Horovod). Experience with LLM fine-tuning and prompt engineering. Contributions to open-source ML projects. ...

with frameworks such as:PyTorchTensorFlowScikit-learnXGBoostExperience deploying ML solutions on AWS, including services such as SageMaker, Lambda, and S3.Strong understanding of MLOps tooling, including MLflow, Weights & Biases, or Data Version Control (DVC).Experience developing LLM or Generative AI applications, including RAG architectures and prompt engineering.Knowledge of LLMOps frameworks such ...

/CD for ML, including automated data validation, model testing, and deployment. Implement ML Engineering solutions using popular MLOps platforms such as AWS Sagemaker , MLflow, Azure ML. End-end custom Sagemaker pipelines for recommendation systemsDesign and implement the engineering components of GAR+RAG systems (e.g., query interpretation and reflection, chunking, embeddings ...

engineers to deliver high-quality scalable data solutions.Technical Expertise• Deep expertise with the Databricks platform (Spark/PySpark/Scala, Delta Lake, Unity Catalog, MLflow).• Proficiency in ETL/ELT tools such as DBT, Matillion, Talend, or equivalent.• Strong SQL and Python (or equivalent language) skills for data manipulation ...

development sectors.Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL, with experience in geospatial and environmental data analysis.Experience with MLOps tools (MLflow, Docker, CI/CD pipelines) and cloud platforms (Azure preferred), ensuring scalable and reliable solutions.Proven ability to influence non-technical stakeholders and communicate complex concepts ...

development sectors.Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL, with experience in geospatial and environmental data analysis.Experience with MLOps tools (MLflow, Docker, CI/CD pipelines) and cloud platforms (Azure preferred), ensuring scalable and reliable solutions.Proven ability to influence non-technical stakeholders and communicate complex concepts ...

PowerBI and TableauExperience deploying models into production using Azure servicesExposure to data engineering fundamentals (e.g., data pipelines, delta lake)Knowledge of experience tracking tools (MLFlow, Azure ML tracking)Connect to your business – TaxInnovation and tax consultancy may not seem to go together, but that’s exactly what you’ll find ...

Candidates should have strong experience in AI Engineering, MLOps and cloud-native AI platforms, alongside hands-on knowledge of technologies such as LangChain, LlamaIndex, MLflow, Kubernetes, Docker and CI/CD tooling. Applicants will ideally demonstrate: * Experience working in a major Consulting firm, and/or in industry * A strong ...

containerisation/orchestration (Docker, Kubernetes). Strong understanding of data modelling, storage systems, and streaming/processing architectures. Familiarity with ML tooling such as MLflow, ZenML, or Metaflow. Familiarity with ML model lifecycle management (training, deployment, monitoring). Excellent collaboration and communication skills, with the ability to work cross‐functionally. ...

deliver high‐quality scalable data solutions. Technical Expertise Deep expertise with the Databricks platform (Spark/PySpark/Scala, Delta Lake, Unity Catalog, MLflow). Proficiency in ETL/ELT tools such as DBT, Matillion, Talend, or equivalent. Strong SQL and Python (or equivalent language) skills for data manipulation ...