1 to 25 of 92 MLflow Jobs in London

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 ...

pipelines. Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

pipelines. Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

pipelines. Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

pipelines. Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

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 ...

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 ...

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 ...

model optimization, quantization, or deployment to edge devices. Familiarity with MLOps tools and practices: CI/CD, monitoring, and orchestration frameworks (e.g., Kubeflow, Flyte, MLflow); proficiency with Docker and Kubernetes for AI workload containerization. Hands‐on experience designing RAG pipelines and/or multi‐agent systems.. Understanding of sizing ...

machine learning, data mining, information retrieval, and statistics Knowledge of Kubernetes (AWS EKS) Experience with training models in Databricks and SageMaker Experience working with MLFlow Practical RAG experience - retrieval quality, embeddings, and vector stores; Graph RAG a strong plus Expert knowledge of at least one of: AWS, Azure, Kubernetes Knowledge ...

worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ML, Vertex AI or comparable) Are comfortable with ML frameworks such as scikit-learn, PySpark MLlib, or PyTorch/TensorFlow, enough to productionise ...

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 ...

capabilities and skills Proficiency in coding in recent versions of Java and/or Python programming languages Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow) Experience with cloud technologies (AWS/Azure/GCP) and distributed systems, web technologies and event drive ...

discipline (or equivalent experience) Nice to Have: Databricks certification or experience with the Databricks Platform Experience with Unity Catalog, Lakeflow Spark Declarative Pipelines, or MLflow Background at a data/AI company, cloud provider, or technical consulting firm Interview Process: Recruiter Screen → Hiring Manager Screen → Design and Architecture Interview → Live ...

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 ...

with Azure‐based data and ML platforms, ideally including Azure ML, Databricks, Azure Data Factory or Fabric pipelines, Blob/Data Lake storage, and MLflow or similar tooling. Experience building production‐grade ETL/ELT pipelines and supporting ML workloads from ingestion through deployment. Comfort moving between data engineering, machine ...

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 ...

with rigorous validation of code, data transformations and analytical conclusions. Desirable Skills and Experience Experience with Azure Data Lake, Azure Data Factory, dbt, Airflow, MLflow, Power BI, Microsoft Purview or comparable tools. Experience with search indexes, vector databases, graph databases, knowledge graphs, ontologies or master-data-management techniques. Experience with ...

containerisation (Docker/Kubernetes). Strong background in data modelling, warehousing, and supporting analytical and ML workloads. Exposure to MLOps tools including MLFlow, kedro and Amazon Sagemaker. Development and maintenance of real time products like APIs using FastAPI/Flask. Operates autonomously with strong ownership and accountability. Strategic thinker ...

hands-on practice in Python.* Familiarity with popular machine learning libraries and frameworks such as scikit-learn, Hugging Face, PyTorch and TensorFlow.* Experience with MLFlow, AWS SageMaker and Bedrock is preferred.* Familiar with MLOps concept, SQL and PySpark* Experience with feature stores, machine learning models as service, and monitoring dashboards ...

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 ...

where data comes from and how it will be used Even better ifyou have Experience with data versioning and experiment tracking tools e.g. DVC, MLflow, or Dagster asset lineage Experience preparing data for machine learning and longitudinal modelling, for example feature tables and train/test splits that avoid leakage ...