1 to 25 of 40 Remote/Hybrid MLflow Jobs in London

GenAI frameworks (e.g., LangChain, Hugging Face) and cloud-native AI services (AWS, Azure or GCP) Knowledge of MLOps practices and tools like Databricks or MLflow Ability to tackle ambiguous problems with structured, analytical approachesStrong verbal and written communication skills for technical and executive audiences Experience in hybrid consulting environments, balancing ...

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 ...

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 ...

E.g. test-driven development, smart data structure and algorithm selection. Experience in using cloud environment (e.g. Azure, AWS). Experience using Azure Databricks, Azure MLflow, Azure ML services and/or other ML services. Experience using Excel, SQL, Power BI, Tableau. Experience using Docker and Kubernetes. Experience working ...

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 ...

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 ...

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 ...

Solid grasp of MLOps principles with experience deploying models in production Proficiency in Python and tools/platforms such as Azure, AWS, GCP, Databricks, MLFlow, and Airflow Strong communication skills with the ability to engage business and technical stakeholders We offer EPAM Employee Stock Purchase Plan (ESPP) Protection benefits including ...

workloads in production (AWS or similar). Evaluation and observability for AI — hands‐on with tools like LangFuse, LangSmith, Braintrust, or MLflow — plus solid automated testing and CI/CD. Proven technical leadership — mentoring engineers, driving technical vision, and delivering complex, cross‐team projects. English — advanced proficiency, written and spoken. ...

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 ...

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 ...

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 ...

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 ...

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 ...

technical planning, architecture decisions and engineering best practices. Essential experienceStrong Python development skills. Experience with Machine Learning experiment tracking and model registries using MLflow, Weights & Biases, Neptune, ClearML or a similar platform. Experience with distributed training frameworks such as Ray, DeepSpeed, Horovod or PyTorch FSDP.Hands-on experience optimising models ...

observability (Ganglia, Prometheus, or Datadog). Understanding of serverless Databricks clusters. Familiarity with Terraform and infrastructure as code for Databricks resource management. Exposure to MLflow and model-serving pipelines for end-to-end performance optimization. Experience with Unity Catalog and fine-grained governance controls. Qualifications Bachelor's or Master ...

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/ ...

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 ...

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 ...

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 ...

Gemini, Claude and OpenAI APIs. Hands-on experience with AI tools/frameworks (e.g., Braintrust, AutoEval) and/or MLOps tools (e.g., Airflow, MLflow, Vertex AI, or similar). Solid understanding of data engineering practices, including ETL, batch/streaming pipelines, and data quality monitoring. Familiarity with cloud infrastructure ...

with MongoDB or other NoSQL databases Experience with unsupervised methods such as clustering or hidden Markov models Experience with ML lifecycle tooling such as MLflow Experience with anomaly detection A background around boats, cars, bikes or planes We're less interested in ticking off niche experience like maritime or MongoDB ...

binary classification problems Event streaming data engineering tools such as Flink or Spark Experience with MLOps and tools such as Airflow, BentoML and MLFlow Use of Docker CI/CD and SDLC (e.g. JIRA, Git, Jenkins, Octopus) Job type Permanent Skills DataScientist Why Zempler Bank We’re on a journey ...

product engineering—from architectural design and prototyping to CI/CD-enabled deployment—using modern platforms and tools such as Azure ML, Databricks, MLflow, LangChain and LangGraph. Champion automation, testing, and observability across pipelines.· Operational Excellence: Define reusable development patterns, enforce coding standards, and promote MLOps best practices that support … e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers).· Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph.· Proven experience with modern engineering practices Git, version control, unit testing and containerisation.· Familiarity with agile work methodologies and tools like Jira ...