14 of 14 Feature Engineering Jobs in London

Work with large datasets to build predictive and analytical models • Collaborate with data scientists to transition models from research to scalable production systems ML Engineering and Deployment • Design and maintain machine learning pipelines and model deployment infrastructure • Implement automated model training, validation, and monitoring processes • Ensure models are scalable … reliable, and performant in production environments Data Engineering and Infrastructure • Work with data engineers to build efficient data pipelines and feature engineering processes • Optimise data workflows to support model development and experimentation • Contribute to the development of robust ML infrastructure and tooling Collaboration and Product Integration • Work ...

making across complex digital transformation programmes. With access to cutting edge AI frameworks, high performance computing environments, and modern data platforms, you will guide engineering and data science teams in building secure, scalable, and ethical AI systems. This role empowers you to shape end to end AI ecosystems-accelerating … more intelligent, AI enabled future. Key Responsibilities Define the enterprise AI architecture vision and reference patterns; align them to business goals, risk posture, and engineering standards across cloud and hybrid environments Design secure, scalable AI solutions covering data ingestion, feature engineering, model training, inference, and continuous feedback ...

Team The Enterprise Solutions Technology team is dedicated to delivering next-generation, high-scale technology platforms through resilient architecture, data excellence, and engineering innovation. Our mission is to enhance our digital presence and improve customer engagement across various domains, including Lending, Corporate Actions, Tax, Regulatory & Compliance, Regulatory Reporting, Public … machine-learning systems across a complex, regulated financial-services estate. This is a strategy-led and hands-on applied data science and ML engineering role, responsible for defining the AI/ML roadmap for Enterprise Solutions while also building high-rigor analytical and predictive models for anomaly detection, variance ...

building the future of population health management. As a Senior Machine Learning Engineer, you will sit at the intersection of data science, platform engineering, and production systems. Your mission is to design and operate the foundations that allow ML, analytics, and agentic AI systems to move safely and reliably … from experimentation into regulated healthcare production environments on Google Cloud Platform. You will be the primary interface between Data Science and Platform Engineering, enabling rapid iteration while enforcing production and compliance standards. Responsibilities Design and implement Onsera's agentic AI platform – architect LLM/agent framework selection, standardized patterns ...

Connect to your Industry Deloitte’s AI for Sustainability and Infrastructure team sits within our Engineering, AI & Data offering area and is purpose built to help our client’s lead and win through the transition to a low‐carbon, digitally enabled, prosperous, equitable and resilient economy. We take work … transformative AI and Generative AI strategies aligned with business goals. Lead the design, development, and implementation of advanced AI pipelines, encompassing data acquisition, preprocessing, feature engineering, model development, evaluation, and secure deployment at scale. Oversee the development and operationalisation of state-of-the-art AI models, including large ...

capable of reasoning and planning. Expertly handle LLM fine-tuning (PEFT, LoRA, QLoRA) and RAG pipelines. Data Orchestration: Build ETL/ELT pipelines and feature engineering workflows to integrate structured and unstructured data into centralized platforms. End-to-End MLOps: Own the lifecycle—from CI/CD automation … every system is trustworthy, fair, and explainable, implementing quantifiable metrics for bias detection and regulatory compliance. Technical Toolkit Models: LLMs, Generative AI, Agentic workflows. Engineering: PEFT, Vector Databases (Pinecone/Milvus/Weaviate), Prompt Engineering. Ops: Docker, Kubernetes, CI/CD, Experiment Tracking (MLflow/W&B). Data ...

governance frameworks to ensure responsible AI/ML model deployment. Use Python to automate processes and optimise workflows. Handle large datasets, including data preprocessing, feature engineering, and model evaluation. Lead the agile development of AI solutions from concept through deployment and ongoing improvement. Create and maintain comprehensive technical … documentation. Communicate complex AI concepts clearly to stakeholders at all levels. What you'll bring: Proven experience (3 years) in AI solution engineering, with a focus on large language models, prompt engineering, and RAG implementations. Strong skills in data analytics, MLOps practices, and API development. Knowledge of governance ...

logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command of SQL and familiarity with data engineering environments such as Databricks or similar Skilled in data visualisation and storytelling ...

conversational analytics products • Develop LLM-driven systems including fine-tuning and prompt pipelines • Apply statistical modelling and quantitative analysis to financial datasets • Conduct feature engineering, model evaluation, and experimentation • Work with engineering teams to integrate models into AWS-based platforms • Support development of AI systems used ...

problem definition through to deployment in live environments. * Design, build, and deploy machine learning models within Pega-based decisioning platforms. * Lead model development across feature engineering, training, validation, and ongoing performance monitoring. * Work closely with application and logic developers to embed models into operational decision workflows. * Develop statistical ...

This is a senior individual contributor role with strong influence through mentorship, collaboration, and technical leadership. You will work closely with partners across product, engineering, and the business to design and deliver data‐driven solutions.We welcome candidates from a range of backgrounds and experiences who are excited to apply … Development** Lead by example through hands-on coding and model development. Design, prototype, and validate innovative solutions for high-impact problems. Partner with engineering teams to deploy models into production environments.* **Domain Expertise** Leverage deep knowledge of aviation, travel, or related industries to inform modeling approaches and deliver customer ...

driven and AI/ML use cases. Build, train, evaluate, and fine-tune machine learning models for structured and unstructured data. Perform data analysis, feature engineering, and experimentation to validate hypotheses. Develop efficient SQL queries for data extraction, transformation, and analysis from relational databases. Integrate prototypes with APIs ...

integrated applications across on-prem and cloud. Secure MLOps/ML platforms: architect identity, secrets management, network segmentation, and least-privilege access for feature stores, model registries, orchestration, and deployment pipelines. Data protection by design: establish controls for sensitive data ingestion, anonymization/pseudonymization, encryption (at rest/… identity, secrets management, key management (KMS/HSM), network segmentation, and policy-as-code. Strong knowledge of AI/ML workflows: data ingestion/feature engineering, model training/inference, MLOps tooling (model registry, orchestrators, serving). Practical experience with adversarial ML concepts and defenses; familiarity with model ...

experimentation strategy for the product area — designing A/B tests and multi-armed bandit experiments that generate genuine learning about AI feature performance, not just validate existing assumptions Drive the feedback loop strategy — defining how member behaviour is captured, modelled, and fed back into AI ranking systems … context, using results to change direction rather than confirm instinct Technically credible AI and ML fluency — able to read a model card, challenge a feature engineering decision, write testable acceptance criteria for ML features, and explain confidence intervals to non-technical stakeholders, without overstepping into engineering decisions ...