51 to 75 of 121 MLflow Jobs in England

coding assistants in a responsible manner. Familiarity with DevOps practices and tools. Hands-on experience with experimentation and model tracking tools (e.g., MLFlow, Weights & Biases). Demonstrable impact of technical solutions or projects on decision-making. Experience working in fast-paced, agile environments. Customer-facing experience, notably in understanding ...

coding assistants in a responsible manner. Familiarity with DevOps practices and tools. Hands-on experience with experimentation and model tracking tools (e.g., MLFlow, Weights & Biases). Demonstrable impact of technical solutions or projects on decision-making. Experience working in fast-paced, agile environments. Customer-facing experience, notably in understanding ...

native software in production environments Engineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL) Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL LLM tooling: Familiarity with LangChain, Semantic ...

Computer Science, Machine Learning, or a related technical discipline. Public writing, talks, or threads about building with AI. MLOpsand model serving experience (BentoML,MLflow, Vertex AI, SageMaker). Streaming and batch ingestion pipelines (Spark, Airflow, Beam, Glue). Healthcare or life sciences domain exposure. AWS Professional certification or other relevant ...

Capabilities, and Skills: Experience with agent frameworks (e.g., Google ADK, LangGraph) and agent protocols such as MCP, A2A, or AG-UI Experience with Databricks, MLflow, and Delta Lake or Apache Iceberg Exposure to model serving on Kubernetes and to model monitoring (drift, latency, accuracy) Familiarity with CRM platforms such ...

work streams Preferred Qualifications, Capabilities, and Skills Background in STEM with exposure to productionising machine learning systems Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Databricks, BentoML, Arize) Proficiency in cloud-native microservices architecture Hands-on experience with Amazon Web Services (AWS) Previous experience as a Platform engineer ...

their work without line-managing them Experience with a modern cloud data stack, ideally Snowflake (Snowpark, Cortex) and dbt Practical ML Ops experience, e.g. MLflow or similar, Git, CI/CD, containerisation, automated retraining and monitoring Experience with LLM and GenAI tools, both for your own development workflow ...

work independently, navigate ambiguity, resolve blockers, and deliver results with a strong sense of urgency It’s a bonus if you have Experience with MLFlow/model versioning, SageMaker Pipelines, or Databricks Familiarity with real-time, high-volume data products and low-latency data stores (e.g. DynamoDB) Automated testing ...

version control. Experience with AI assisted tooling Hands-on experience taking machine learning models into production and supporting them once live. Experience with MLflow or similar model lifecycle tooling, including deploying models for batch and/or near real-time use cases. Experience building or working with CI/ ...

working out what to build. Excellent communication skills, including explaining technical trade‐offs to non‐technical colleagues. Experience with Databricks (MLflow, Delta Lake, model serving). Solid ML fundamentals; exposure to PyTorch or classical ML is useful but not central to this role. Experience with multi‐modal models, particularly document ...

with data engineers on performance optimization, cost control, and platform capabilities MLOps & AI Platform (20%) Build ML model deployment pipelines using Azure ML, Databricks MLflow, or both Implement model versioning, experiment tracking, and model registry patterns Establish inference endpoints (batch and real‐time) with proper monitoring and governance Create reusable ...

including Python, APIs, and distributed systems. Good understanding of MLOps tooling, including CI/CD, monitoring, and pipeline management. Experience with tools such as MLflow, Airflow, Evidently, DVC, Docker, or Kubernetes. Experience working within AWS, GCP, or Azure environments. Previous mentoring or leadership experience with an interest in developing management ...

requirements and communicate findings effectively. Stay current with developments in AI and machine learning through continuous learning. Technical Stack Core: Power BI LangChain basics MLflow Databricks Hugging Face Transformers What You'll Bring Hands-on experience in data science or applied analytics. Solid Python programming skills and understanding of machine ...

experimentation and continuous learning. Mentor junior data scientists and contribute to knowledge sharing across the team. Technical Stack Core: Databricks Power BI REST APIs MLflow Hugging Face Transformers Microsoft Fabric Docker CI/CD with Azure DevOps Power Automate integration for AI workflows What You'll Bring Hands‐on experience ...

Data Mesh concepts, data products, or federated data governance models. AI/ML Infrastructure: Exposure to AI/ML data foundations, including feature stores, MLflow, model serving, or retrieval pipelines (RAG). Regulated Sectors: Experience working within regulated environments (Financial Services, Healthcare, Public Sector). How You Work Pragmatic & Objective ...

Streaming) Data products, mesh or federated ownership models applied on a real programme, not just in theory ML and AI platform work: feature stores, MLflow, model serving, and the data foundations behind RAG or agentic use cases Regulated-sector exposure — financial services, healthcare, public sector — and the assurance that comes ...

Excellent understanding of statistics, machine learning methodologies, and model evaluation. Experience delivering AI solutions from proof-of-concept through to production. Exposure to Databricks, MLflow, Spark, or similar cloud-based data platforms. Ability to influence senior stakeholders and lead technical strategy Desirable Experience Experience building multi-agent AI systems ...

Demonstrate familiarity with Markets Operations processes such as trade lifecycle, post-trade operations, reconciliations, controls, or exception management Use AWS and tools such as MLflow Develop front-end capabilities with React or modern front-end development Apply graph database modeling concepts Why Join Us? Your contributions will help redefine what ...

/TF a plus) and SQL; rigorous evaluation (MAE/RMSE, MAPE, pinball loss) Experience productionising models (APIs/batch jobs), experiment tracking (e.g., MLflow) and basic MLOps Being able to evaluate the right tools for the job given business requirements & experiment with new ways of doing things to continuously ...

deployment Familiarity with MLOps concepts (CI/CD pipelines, model registries, automated testing) — awareness required, not hands‐on ownership Experience with ML lifecycle tooling — MLflow, Azure ML, or equivalent Python proficiency — able to read and review analytical code, contribute where needed Consulting or forward‐deployed engineering background (Palantir, QuantumBlack ...

methodologies for validation and processing of data against contracts. Enablement: Establish a stable data serving layer for Grafana and Analytics (via Databricks/MLflow), unblocking the Data Science team. Mentorship: Upskill the existing team in good practices (dbt, git workflows, SQL optimisation, data modelling). Experience & Background Pragmatic Architect: Capable ...

deployments Strong Python (pandas, scikit-learn, PyTorch) and SQL Solid grounding in statistics, experimentation and causal inference Experience with at least one MLOps stack (MLflow, SageMaker, Vertex) Nice to have Published research or Kaggle competition experience Domain depth in financial services, retail or healthcare Send your CV and a short ...

architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. Knowledge of AI platform tooling such as Azure ML, Amazon SageMaker, MLflow, Kubeflow and Databricks . Ability to evaluate technologies and make evidence-based architectural decisions. Experience designing secure, scalable and cost-optimised AI infrastructure. Strong architecture documentation ...

architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. Knowledge of AI platform tooling such as Azure ML, Amazon SageMaker, MLflow, Kubeflow and Databricks . Ability to evaluate technologies and make evidence-based architectural decisions. Experience designing secure, scalable and cost-optimised AI infrastructure. Strong architecture documentation ...

regulated workflows requiring auditable LLM output Experience with OpenFGA or OPA/Rego, service mesh technologies, and micro-VM isolation Experience with Databricks, MLflow, and model serving on Kubernetes, including GPU capacity planning and small language model inference Experience with knowledge graphs, GraphRAG, or organizational memory for agents Experience with ...