4 of 4 MLflow Jobs in the East of England

model risk reviews, security assessments, and secure deployment practices across client environments Stand up MLOps practices – experiment tracking, model versioning, evaluation, and monitoring (e.g. MLflow, Weights & Biases) Monitor deployed models and pipelines for drift, degradation, data issues, and cost, and iterate to improve them Containerise and deploy workloads with Docker … this role at present Desirable (Nice to Have) Experience with streaming and big‐data tooling – Apache Kafka, Spark, or Flink Experience with MLOps platforms – MLflow, Kubeflow, Vertex AI, SageMaker, or Weights & Biases Experience evaluating and optimising RAG systems at scale (retrieval quality, chunking, re‐ranking) Experience designing multi‐agent systems ...

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 ...

have a good knowledge of modern software engineering best practices, microservices, TDD/DDD, common Design Patterns Experience with Databricks, PostgreSQL, Amazon RedShift or MLflow would be great but not essential What's in it for you: As a Python Software Engineer (Machine Learning AWS) you will earn a competitive ...

scientific data, and that Applied AI and data engineering teams build against internally. Own the end-to-end model and prompt lifecycle across Databricks MLflow and AWS Bedrock, including registration, versioning, asset bundles, staged promotion, rollback, and multi-model serving. Design the inference substrate for both real-time and batch … critically evaluate AI-generated code for correctness, security, and architectural fit. Production-level experience with a model registry and serving stack, ideally Databricks MLflow, including model registration, versioning, asset bundles, and serving workflows. Experience treating AI evaluation as a release gate rather than post-hoc reporting, including eval harnesses, regression ...