10 of 10 Model Validation Jobs in the East of England

maintaining production-grade, distributed ML systems (Sagemaker) Deep MLOps Proficiency: Expert-level knowledge of CI/CD, orchestration (e.g., Apache Airflow, Flink), and model monitoring/drift detection at scale. Software Engineering Rigor: Strong background in Python, distributed systems, and backend development, with a firm grasp of software engineering … learning role, with demonstrated success in ML Engineering and deploying models to production. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing). Great understanding of machine learning principles (experimental design, statistical distributions and test, machine learning algorithms) Expertise in deploying ...

maintaining production-grade, distributed ML systems (Sagemaker) Deep MLOps Proficiency: Expert-level knowledge of CI/CD, orchestration (e.g., Apache Airflow, Flink), and model monitoring/drift detection at scale. Software Engineering Rigor: Strong background in Python, distributed systems, and backend development, with a firm grasp of software engineering … learning role, with demonstrated success in ML Engineering and deploying models to production. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing). Great understanding of machine learning principles (experimental design, statistical distributions and test, machine learning algorithms) Expertise in deploying ...

explain complex analysis clearly. Support modelling tasks such as clustering, segmentation, site scoring, similarity matching and offer-fit analysis. Help validate results, sense-check model outputs and explain to a non technical audience Contribute reusable code and documentation that reduces manual rework on future projects. Collaborate with colleagues through ...

prototyping how they can be applied to our productsOperations: Optionally contribute to our AI infrastructure to help operate self-hosted open-source modelsTesting & Validation: Development of tooling to assist our Test Team in conducting evals against our many provider models to ensure we are continually optimising for quality ...

they can be applied to our products Operations: Optionally contribute to our AI infrastructure to help operate self-hosted open-source models Testing & Validation: Development of tooling to assist our Test Team in conducting evals against our many provider models to ensure we are continually optimising for quality ...

assessment, fraud detection, forecasting, optimization, and operational efficiency. Design end-to-end problems spanning the complete data science pipeline (data ingestion → cleaning → EDA → modeling → validation → deployment considerations). Incorporate big data processing scenarios requiring scalable computational approaches. Verify solutions using Python with standard data science libraries and statistical methods. … post, qualify, and get the chance to contribute to projects aligned with your skills, on your own schedule. From creating training prompts to refining model responses, you'll help shape the future of AI while ensuring technology benefits everyone. You hold a Master's or PhD Degree in Data ...

biomass/canopy-height estimation. Build embedding-driven workflows on top of EO foundation models - few-shot classifiers, similarity search, downstream regressors. Design validation strategies that benchmark outputs against plot inventories and third-party reference datasets, quantify uncertainty, and surface failure modes; produce QA artefacts (maps, plots, model ...

live, and monitoring performance afterwards. You'll join client calls, build relationships, and understand what actually matters on the ground - not just whether the model is accurate, but whether the kitchen prepped the right amount of food. You'll love this role if: You enjoy taking ownership … data, fail gracefully, and need monitoring Strong ML fundamentals - you can reason about trade-offs in practice, explain why certain features matter more than model selection, and make good judgement calls when something unexpected happens Client-facing deployment experience - you've personally owned an ML deployment ...

Work (High-Level Focus Areas) Release Management Review & Improvement Assess current release processes (CAB, approvals, deployments, communication) Define an end-to-end release lifecycle model Standardise release types (standard/normal/emergency) Introduce clear release readiness criteria and approval controls Align release processes with CI/CD pipelines … change management Define release cadence and governance model Implement post-release validation and feedback loops Establish release metrics Operating Model & Process Integration Define end-to-end lifecycle (demand → build → test → deploy → support) Establish clear handoffs and ownership between IT, Product, and QA Remove siloed or disconnected processes ...

Work (High-Level Focus Areas) Release Management Review & Improvement Assess current release processes (CAB, approvals, deployments, communication) Define an end-to-end release lifecycle model Standardise release types (standard/normal/emergency) Introduce clear release readiness criteria and approval controls Align release processes with CI/CD pipelines … change management Define release cadence and governance model Implement post-release validation and feedback loops Establish release metrics Operating Model & Process Integration Define end-to-end lifecycle (demand build test deploy support) Establish clear handoffs and ownership between IT, Product, and QA Remove siloed or disconnected processes ...