7 of 7 MLflow Jobs in the City of London

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 knowledge ...

LangGraph (agent architecture design, state management, and graph-based workflow orchestration). ML & Cloud Ecosystems: Solid understanding of ML algorithms, FastAPI, PyTorch/TensorFlow, MLflow, MLOps practices, containerization (Docker, Kubernetes/AWS EKS), and cloud services (AWS Bedrock, SageMaker, Azure AI Search). Soft Skills: Excellent communication and collaboration skills ...

Databricks, Azure ML, and ideally Azure AI Foundry Strong experience deploying and managing LLMs and machine learning models in enterprise cloud environments Experience using MLflow for experiment tracking, model lifecycle management, and versioning of both traditional ML models and LLM-based solutions Strong understanding of LLMOps practices, including deployment, monitoring ...

lifecycle best practices Hands-on experience with ML libraries: PyTorch, PySpark, scikit-learn, Hugging Face Transformers Proficiency in AI tooling: Azure ML, Databricks, MLflow, LangChain, LangGraph Proven experience with Git, unit testing, containerisation and agile methodologies (Jira, Confluence) Good knowledge of generative AI, machine learning, deep learning or NLP 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 ...

pipelines Build and maintain CI/CD pipelines for inference services, retrievers, ingestion workflows, and RAG components Implement observability across AI workloads using CloudWatch, MLflow, and OpenTelemetry - covering latency, throughput, cost, and system health Apply secure-by-design principles including IAM, encryption, network controls, and audit logging Work closely with ...

libraries, frameworks and tools such as LangChain, Llamaindex, Aider, Claude Code Best practices and approaches to model performance and benchmarking, including tools like MLFlow Containerisation and orchestration technologies like Docker/Kubernetes/Helm/Flux Data visualisation tools and notebooking tools such as Marimo/Jupyter ETL/ ...