1 to 25 of 47 MLflow Jobs in the UK

/C++, Java, Go Machine Learning & AI stacks, including: TensorFlow, PyTorch, Scikit-learn, Transformers, LLMs. Customization: fine-tuning, RAG, other related methods. MLOps & DevOps: MLflow, TFX, Kubeflow, Docker, Kubernetes, CI/CD terminology and pipelines in GIT, Jira Cloud & Infrastructure: AWS BedRock, SageMaker AI, Azure Machine Learning, GCP Vertex ...

Purview) Drive scalable lakehouse architectures (Delta Lake, Iceberg, Hudi) Cloud, Infrastructure & MLOps Champion multi-cloud engineering across Azure, AWS, and GCP Lead MLOps strategy (MLflow, Kubeflow, SageMaker, Vertex AI) and CI/CD pipelines Ensure scalable deployments using Kubernetes, Docker, and Infrastructure-as-Code Enterprise Integration (.NET Focus) Integrate ...

field Proven experience building and implementing LLM-based solutions (GPT, Claude, Llama, Mistral, or similar) Hands‐on experience with at least one of: Databricks (MLflow, AutoML), Azure Machine Learning, or Snowflake (Snowpark ML, Cortex) Understanding of natural language processing, computer vision, and recommender systems Strong programming skills in Python ...

field Proven experience building and implementing LLM‐based solutions (GPT, Claude, Llama, Mistral, or similar) Hands‐on experience with at least one of: Databricks (MLflow, AutoML), Azure Machine Learning, or Snowflake (Snowpark ML, Cortex) Understanding of natural language processing, computer vision, and recommender systems Strong programming skills in Python ...

libraries (scikit-learn, numpy, LightlGBM, Pandas, LangChain/LangGraph, TensorFlow, etc..) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, an EV Scheme ...

techniques such as fine-tuning and RAG. Experience deploying AI solutions in AWS, Azure, or GCP environments. Strong MLOps knowledge, including Docker, Kubernetes, MLflow, and CI/CD pipelines. Experience with data engineering, optimisation, or reinforcement learning. Good understanding of the Software Development Lifecycle (SDLC). Why Join: Remote working ...

like BentoML or equivalent. Familiarity with vector databases and retrieval pipelines for RAG architectures. Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps tooling (MLflow, Kubeflow, or similar). Familiarity with voice‐to‐text, IVR, and/or computer vision systems is a plus. Strong understanding of software engineering best ...

containerisation (Docker, Kubernetes). Solid understanding of ML fundamentals (model training, evaluation, common architectures). Desired Skills (Bonus Points) Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or similar). Building data pipelines (real‐time or batch) using tools like Apache Spark, Kafka, Airflow, or dbt. Familiarity with recommender systems ...

Bicep) to automate cloud deployments. CI/CD pipelines with Azure DevOps or GitHub Actions for automated deployment of data pipelines. MLOps best practices — MLflow, Databricks Model Serving, Feature Store. Knowledge of IFRS 17, BCBS 239, UK Data Protection Act and Solvency II compliance. Experience with pricing models, claims processing ...

Bicep) to automate cloud deployments. CI/CD pipelines with Azure DevOps or GitHub Actions for automated deployment of data pipelines. MLOps best practices — MLflow, Databricks Model Serving, Feature Store. Knowledge of IFRS 17, BCBS 239, UK Data Protection Act and Solvency II compliance. Experience with pricing models, claims processing ...

lifecycle best practices Hands-on experience with ML libraries: PyTorch, PySpark, scikit-learn, Hugging Face Transformers Proficiency in AI tooling: Azure ML, Databricks, MLflow, LangChain, LangGraph Proven experience with Git, unit testing, containerisation and agile methodologies (Jira, Confluence) Good knowledge of generative AI, machine learning, deep learning or NLP Strong ...

Neptune, or Blazegraph. Experience with embeddings, vector databases, RAG, LLM-based enrichment, or knowledge graph completion. Experience with MLOps tools such as Docker, Kubernetes, MLflow, Weights & Biases, Airflow, or similar. Experience with AWS, GCP, or Azure. Experience in construction, architecture, BIM, building materials, technical product data, catalogues, standards, or compliance ...

pipelines. Strong proficiency in Python, R, and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with cloud platforms and ML infrastructure (AWS SageMaker, MLflow). Practical understanding of monitoring, logging, and CI/CD. Experience with LLMs, vector search, or retrieval-augmented systems. Comfortable working with structured and unstructured data. ...

building and maintaining ETL/ELT pipelines connecting messy source systems to AI applications SQL: Solid querying and data modelling skills AI model management: MLFlow, Hugging Face, or equivalent Nice to have Experience with time-series modelling, anomaly detection, or forecasting in operational or industrial contexts Infrastructure as Code (Terraform ...

Computer Science, Data Science, Bioinformatics, Engineering, or a related field, or equivalent experience. Proven experience designing and deploying MLOps pipelines, including 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 ...

learn, XGBoost, LightGBM, CatBoost, TensorFlow, Keras, PyTorch. Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe. Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines. Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version control (e.g., Git). Excellent ...

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 ...

following areas: object detection, segmentation, OCR, tracking, image classification, or video analytics. Experience with ML Ops practices and tools such as Docker, Kubernetes, MLflow, and Weights & Biases. Systematic approach to model evaluation, benchmarking, data quality control, and error analysis. Familiarity with GPU/CPU inference optimization, latency management, and model ...

scikit-learn, NumPy, LightGBM, Pandas, LangChain/LangGraph, TensorFlow, etc.) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc MLOps/DevOps: Terraform, Docker, Airflow, MLFlow Monitoring and evaluation platforms including New Relic and Braintrust More information Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy ...

risk management, and compliance (Zero Trust architectures, identity‐aware AI access, secrets management) AI/ML platforms, responsible AI practices, and model lifecycle management (MLflow, Vertex AI, SageMaker, Azure ML, Arize, Azure AI Studio) Design and operationalize enterprise‐scale GenAI and agent‐based solutions. Experience with modern AI architectures including ...

Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale-up settings Company ...

pipelines Build and maintain CI/CD pipelines for inference services, retrievers, ingestion workflows, and RAG components Implement observability across AI workloads using CloudWatch, MLflow, and OpenTelemetry - covering latency, throughput, cost, and system health Apply secure-by-design principles including IAM, encryption, network controls, and audit logging Work closely with ...

environment It would be helpful if you also have experience with : RAG, LLMs, embeddings, vector databases or agent-based systems MLOps tools such as MLflow, Kubeflow or similar Docker, Kubernetes or containerised deployment environments Simulation, optimisation, decision-support or modelling techniques Secure development practices Working with complex, regulated or high ...

GraphQL APIs Cloud & Infra: AWS (ECS, S3, Lambda), Docker, Cloudformation, Pulumi, CI/CD with GitHub Actions ML/AI: PyTorch, TensorFlow, scikit‐learn, MLflow for experiment tracking, Zenml for ML pipelines Data: PostgreSQL, DynamoDB, data pipelines with Zenml (We’re open to evolving the stack and expect ...

Azure) and tooling (Docker, Kubernetes, Terraform, Helm).* Familiarity with frameworks such as **Spring (AI/Boot/Core), Flask, LangChain, LangGraph, ADK**, MLFlow, and orchestrators like **N8N**.* Experience with **React** and modern UI frameworks (Gradio, Streamlit).* Understanding of **REST/WebSocket APIs**, CI/CD pipelines ...