1 to 25 of 38 MLflow Jobs in Central London

/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3, Lambda, SageMaker), Kubernetes, Terraform/CDK ML Ops & Automation: MLFlow, feature stores, model monitoring, A/B testing Data Security & Compliance: Secure SDLC, ISO/NIST, data governance, GDPR-compliant pipelines Collaboration & Control: Git-based ...

data modeling. Familiarity with cloud platforms (AWS, Azure, GCP) for deploying ML and data solutions. Knowledge of MLOps practices and tools, such as MLflow or Kubeflow. Strong problem-solving skills, with the ability to troubleshoot both ML models and data systems. A collaborative mindset and ability to work ...

workflows. ML Ops & deployment: Familiarity with deploying models into production using APIs or microservices, and applying ML Ops practices such as experiment tracking (e.g., MLflow, Weights & Biases), model versioning, and performance monitoring. Experience collaborating with engineering teams to ensure scalable and maintainable deployment. Backend & service development: Experience building production-grade ...

Solid understanding of supervised/unsupervised learning, deep learning, and model evaluation techniques. Cloud & MLOps: Experience with AWS, GCP, or Azure; Docker/Kubernetes; MLflow or similar tools. Data Handling: Familiarity with big data frameworks (Spark, Hadoop) and data versioning tools (DVC). Soft Skills: Strong problem-solving, communication ...

data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/CD pipelines for automated model training and deployment. Understanding ...

data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/CD pipelines for automated model training and deployment. Understanding ...

statistical evaluation Proven experience moving models from experimentation into production with appropriate governance and quality controls Hands-on experience with MLOps tooling such as MLflow, Weights & Biases or Data Version Control Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation (RAG) Familiarity with LLMOps frameworks ...

learning and improvement. Your Profile Essential skills/knowledge/experience: Knowledge of machine learning frameworks (e.g., PySpark, PyTorch ) and model deployment tools (e.g., MLflow, TensorFlow Serving). Strong experience with data processing frameworks (e.g., Apache Spark, Flink). Expertise in SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Cassandra ...

Python (pandas, NumPy, scikit-learn; PyTorch/TF a plus). Feature engineering, data quality validation, model training and optimisation. MLOps: experiment tracking (eg, MLflow), model registry, CI/CD for ML. Serving and orchestration: AWS SageMaker, EKS, batch & Real Time inference. Data pipelines: Spark/SQL, orchestration (Airflow), monitoring ...

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, or Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large, complex organisations or regulated industries. Strong working ...

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 It Would Be Great If You Also ...

algorithms. Consulting Experience: Proven track record of delivering solutions in a client-facing consulting environment. MLOps Expertise: Hands-on experience with tools such as MLflow, Kubeflow, TensorFlow Serving, or similar. Cloud Platforms: Proficiency in multi-cloud environments (AWS, Azure, GCP). DevOps & Automation: Strong knowledge of CI/CD pipelines ...

Langsmith/Langfuse, Gen AI/Agentic AI, Cloud platforms (Azure AI Foundry, AWS Sagemaker/Bedrock, GCP Vertex AI), MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Preferred Delivered AI projects within Agile frameworks Experience on Model Feedback Analysis, topic modelling, sentiment analysis Knowledge of AgentOps and OpenTelemetry ...

teams and delivering enterprise-grade data solutions. Preferred Qualifications Databricks certifications (e.g., Databricks Certified Data Engineer Professional, Architect ). Experience building ML solutions using MLflow or integrating with cloud ML services. Experience in BFSI, Retail, Telecom, or Healthcare data environments. Experience with real-time data processing (Kafka, EventHub, Kinesis). ...

statistical evaluation. Proven track record transitioning models from experimentation to production with governance and quality controls. Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC). Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures. Familiarity with LLMOps tooling (LangSmith, LangChain, LangGraph). Understanding ...

experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation ...

experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation ...

scalable, maintainable, and production-ready AI systems. Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow). Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK ...

lifecycle management Lead documentation, knowledge transfer, and mentoring to upskill internal teams Technical requirements Strong Python and ML frameworks (PyTorch, TensorFlow) MLOps tooling (MLflow, Kubeflow or similar) Docker, Kubernetes, CI/CD pipelines Cloud platforms (AWS, Azure or GCP); Terraform experience preferred Financial services experience highly desirable A hands ...

Tools: Airflow, dbt, Databricks, Snowflake Developer Tooling & CI/CD: GitHub, Azure DevOps, Terraform (or other Infrastructure-as-Code tools) MLOps/LLMOps Frameworks: MLflow, AzureML, LangSmith ...

users. Strong proficiency in containerisation (Docker, Kubernetes) and orchestration of multi-stage ML workflows. Hands-on experience with ML platforms and tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or similar model management systems. Practical knowledge of infrastructure as code, CI/CD best practices, and cloud platforms ...

Production experience with AWS ML services (e.g. SageMaker, Lambda, S3) Proven ability to move models from experimentation into live systems Experience with MLOps tools (MLflow, Weights & Biases, DVC, or similar) Hands-on exposure to LLM/GenAI applications, including RAG Nice to have: Experience with advanced LLM techniques (e.g. agents ...

experience with Python ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost). Experience with AWS ML services (SageMaker, Lambda, S3) and MLOps/LLMOps tooling (MLflow, Weights & Biases, DVC). Proven track record transitioning models from experimentation to production with governance and quality controls. Experience developing LLM/GenAI solutions ...

similar distributed compute frameworks. Experience managing cloud infrastructure, Kubernetes, and CI/CD pipelines. Familiar with dataset versioning, orchestration, and experiment tracking (DVC, MLflow, Airflow). Ideally, we are looking for someone with prior experience working at an early-stage start-up. ...

pipelines for analytics and modelling workflows. Model development & deployment: Familiarity with deploying models into production environments, collaborating with engineering teams, and using tools like MLflow or Weights & Biases for experiment tracking and reproducibility. Proof of work in cloud environments, especially MS Azure, is a plus. Research mindset with business impact ...