1 to 25 of 52 Remote/Hybrid MLflow Jobs in England

functional teams. Desirableskills andexperience: Experience building platforms or applications that support Data Science, Machine Learning, or AI workloads. Experience with MLOps tooling such as MLflow, Kubeflow, Vertex AI, or similar technologies. Knowledge of workflow orchestration platforms such as Apache Airflow. Experience with data processing technologies such as Pandas, Apache Beam ...

Studio, OpenAI, AKS), GCP (Vertex AI, Cloud Run), AWS (Bedrock, SageMaker)* Experience building and automating AI/ML pipelines using tools such as MLflow, Kubeflow, Azure ML, Vertex Pipelines, Airflow or Google ADK* Hands-on experience with Generative and Agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, Autogen, Google ...

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 Familiarity with LLMOps tooling such as LangSmith, LangChain ...

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 Familiarity with LLMOps tooling such as LangSmith, LangChain ...

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 Familiarity with LLMOps tooling such as LangSmith, LangChain ...

with technical and business stakeholders. Desirable Experience with Unity Catalog or similar data governance frameworks. Experience with Terraform or Databricks Asset Bundles. Exposure to MLflow, MLOps or AI and Generative AI workloads. Experience with streaming technologies such as Kafka or Structured Streaming. Databricks certification. Previous consulting or client-facing delivery ...

agent SDKs or similar), and an informed view of when to use them and when not Experience with evaluation and observability tooling such as MLflow, LangSmith or Arize Experience configuring agentic coding assistants for a team, with shared instructions, skills and workflows Solid grounding in machine learning and NLP: able ...

enhance your day-to-day work. Technologies and Tools Python Machine learning frameworks (e.g. PyTorch, TensorFlow, scikit-learn) ML/MLOps tooling (e.g. SageMaker, MLflow, TFServing) Spark and Databricks AWS services (e.g. IAM, S3, Redis, ECS) CI/CD tooling and best practices Streaming and batch data systems (e.g. Kafka ...

Fluent English – for international communication and collaboration. Additional Information Nice to have: Experience with LLMs, or Time Series, Familiarity with tools like Docker, MLflow, Airflow, AWS/GCP/Azure, Background in research or R&D, Experience working in cross-functional or international teams. Soft Skills: Curiosity and problem-solving ...

Comfortable working with technical and non-technical stakeholders to deliver high-quality solutions. Even better if you have Databricks certifications. Experience with Unity Catalog, MLflow or Databricks Asset Bundles. Familiarity with dbt or similar data transformation frameworks. Exposure to Kafka, Event Hubs or other streaming technologies. Experience working with retail ...

modelling, ETL and ELT processes, data quality, security and governance Ability to collaborate effectively with technical and non-technical stakeholders Experience with Unity Catalog, MLflow or Databricks Asset Bundles would be advantageous Knowledge of dbt, Kafka, Event Hubs or other data transformation and streaming technologies would be beneficial Desirable Experience ...

software engineering skills in Python, with an understanding of how to write maintainable, testable code Familiarity with ML tooling and platforms such as Databricks, MLflow, Airflow, or equivalent Familiarity with CI/CD pipelines and how they apply to ML systems An understanding of ML lifecycle challenges - versioning, testing, monitoring ...

OpenAI, AI Foundry or AI Search, with Docker and Kubernetes Understanding of schema validation frameworks (e.g., JSON Schema, Pydantic) and MLOps tools such as MLflow or Airflow Strong communication ability to explain trade-offs around cost, latency and accuracy to technical and non-technical audiences Nice to have Experience using ...

refine problem statements and figure out how to solve them Some extra skills that are great (but not essential): Experience with MLOps tools: Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CD Prior experience in the fraud domain and a strong understanding of fraud detection techniques Experience designing ...

science stack (pandas, scikit‐learn, Jupyter, etc.)and version control tools like Git. Exposureto LLMOps, model monitoring, CI/CD and related technologies (Docker, MLflow,Kubernetes, FastAPI, etc.). Knowledgeand working experience of AGILE methodologies. At Ageas we offer a wide range of benefits to support you and your family ...

stack (pandas, scikit‐learn, Jupyter, etc.) and version control tools like Git. Exposure to LLMOps, model monitoring, CI/CD and related technologies (Docker, MLflow, Kubernetes, FastAPI, etc.). Knowledge and working experience of AGILE methodologies. At Ageas we offer a wide range of benefits to support you and your ...

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 ...

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 ...

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/ ...

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 ...

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 ...

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 ...