24 of 24 MLflow Jobs in London

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and ability More ❯

engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps) Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow) Distributed computing frameworks (e.g., Spark, Dask) Cloud platforms (e.g., AWS, Azure, GCP) and HP computing Containerization and orchestration (Docker, Kubernetes) Ability to scope and effectively deliver projects What we offer More ❯

Build APIs and microservices for scalable AI deployment. Use AI-powered dev tools like GitHub Copilot, Cursor, and Codeium to speed up iteration. Apply MLOps/LLMOps practices with MLflow, Weights & Biases, and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data engineering. Familiarity with vector databases More ❯

Skills: Experience using R and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel More ❯

Skills: Experience using R and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel More ❯

Skills: Experience using R and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel More ❯

Skills: Experience using R and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel More ❯

Knowledge of software engineering best practices, version control (Git), or CI/CD pipelines Understanding of deep learning, NLP, or computer vision Familiarity with deployment tools like Docker or MLflow What You’ll Gain Hands-on Experience : Work on real ML systems and contribute to production-grade solutions Mentorship & Training : Learn from senior ML engineers and participate in structured onboarding More ❯

partners. Nice to have Experience with quality weighted bidding, uplift modelling, or reinforcement style policy optimisation. Familiarity with MMM, MTA, and experiment design in marketing contexts. Vertex AI or MLflow for training and deployment. Containerisation and service reliability skills. Why join us Monthly long weekends: every third Friday off. Wellness stipend and comprehensive parental leave policies. Remote first culture with More ❯

the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and similar for LLMOps The difference to our "AI Engineer" role is: Do you "use/consume" these technologies, or are you the More ❯

Implement rigorous code quality and testing standards across data science projects Support talent acquisition and continuous learning initiatives Knowledge and Experience Knowledge of ML model development and deployment frameworks (MLFlow, Kubeflow Advanced data querying (SQL) and data engineering pipelines (Airflow Extensive experience with comprehensive unit testing, integration testing, and test coverage strategies Experience working with Product Management teams and ability More ❯

MLOps Knowledge Experience taking models from experiments through to production deployments, with tools such as Docker, Kubernetes & serverless alternatives such as AWS Lambda. Familiarity with MLOps tools such as MLFlow, Kubeflow or Sagemaker. A strong knowledge of cloud platforms (ideally AWS) and their respective services for deploying robust, AI-heavy applications. Bonus Experience Experience with named entity recognition/recommendation More ❯

in programming languages such as Python or R, with extensive experience with LLMs, ML algorithms, and models. Experience with cloud services like Azure ML Studio, Azure Functions, Azure Pipelines, MLflow, Azure Databricks, etc., is a plus. Experience working in Azure/Microsoft environments is considered a real plus. Proven understanding of data science methods for analyzing and making sense of More ❯

A/B testing Experiment design and hypothesis testing MLOps & Engineering Scalable ML systems (batch and real-time) ML pipelines, CI/CD, monitoring, deployment Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, Kubernetes Strategic skills Align ML initiatives with business goals Prioritize projects based on ROI, feasibility, and risk Understand market trends and competitive ML strategies Communicate ML impact More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

channels. What you’ll need: • Python, SQL, and data modelling expertise • Advanced knowledge of Snowflake & Snowpark • Confident communicator who can influence and collaborate • Experience building and managing ML environments (MLflow or similar) • Familiarity with CI/CD practices What you’ll do: • Architect and implement scalable ML environments with Data Science • Define best practices for deployment, monitoring, and governance • Build More ❯

personalised experiences across global brands and channels. What you'll need Python, SQL, data modelling Expertise in Snowflake & Snowpark Immaculate communication to influence and collaborate Experience building ML environments (MLflow or similar) CI/CD practices What you'll do Design and implement a scalable ML environment with Data Science Define best practice for deployment, monitoring, and governance Build pipelines More ❯

LangChain/LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI/CD (GitHub Actions) or similar would be a benefit to an application The employer is well known not only for the forward-thinking approach they have More ❯

modal models that combine vision and language Strong grasp of data-centric AI practices - annotation tooling, prompt evaluation, and dataset curation Familiarity with MLOps tools (e.g. Weights & Biases, SageMaker, MLflow) Experience working in regulated sectors like insurance, banking, or property What You'll Be Doing This is a hands-on, high-impact role - you'll be building production-grade AI More ❯