10 of 10 Permanent Feature Engineering Jobs in London

Principal, AI/ML Engineer leads the design, delivery and continuous improvement of AI and ML solutions within their area of responsibility, aligning engineering activities to the Bank’s broader AI strategy and long-term objectives. The role combines strong technical leadership with a clear focus on delivery outcomes … operates with a clear understanding of the wider technology landscape, contributing to strategic direction while taking accountability for translating this into practical, high-quality engineering outcomes across squads. Leads the definition and execution of AI/ML engineering direction across one or more squads, ensuring alignment with ...

taxonomies, citations, metadata, and content from every scientific discipline. Apply the right technique to each problem, using approaches such as classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, LLMs, retrieval, and generative AI. Develop capabilities for semantic search, information retrieval, entity extraction, content classification, recommendation, ranking, summarization … inference, experimentation, monitoring, and continuous improvement. Support deployment, monitoring, model maintenance, drift detection, automated retraining, and ongoing optimization of data science systems. Collaborate with engineering, product, UX, analytics, research, and domain experts, and communicate technical concepts, model behavior, insights, trade-offs, and recommendations clearly to technical and non-technical ...

before production deployment. Develop robust ML pipelines using AWS services and MLOps tooling. Implement experiment tracking, model versioning, monitoring, and reproducibility best practices. Build feature engineering pipelines and contribute to feature store development. Monitor production models, optimise performance, and drive continuous improvements. Apply Responsible AI principles, including … technical concepts to both technical and non-technical stakeholders. Desirable Experience AI agents and agentic workflows. Vector databases such as Pinecone, Weaviate, or pgvector. Feature stores including Feast or AWS Feature Store. Docker, Kubernetes, or Amazon ECS. Infrastructure as Code using Terraform or CloudFormation. Big data processing technologies ...

projects end to end, from problem discovery and technical design through to delivery and measuring impact. Collaborate with Product, Compliance, Operations and other engineering teams while maintaining high standards for reliability, testing and code quality. What do you need? A strong product mindset and passion for user experience … understanding of JDBC and database internals. Experience with CI/CD pipelines and Distributed and Concurrent Systems Experience with machine learning basics (data pipelines, feature engineering, recall/precision, familiarity with machine learning systems in production) is a huge plus Being able to work independently on customer problems ...

concept into production environments Develop scalable ML pipelines using AWS cloud technologies Implement MLOps and LLMOps best practices for deployment, monitoring and governance Support feature engineering, model optimisation and performance monitoring Apply responsible AI principles, including model explainability and fairness Present findings and recommendations to technical … tools such as MLflow, Weights & Biases or Data Version Control Experience designing, testing and evaluating machine learning experiments Knowledge of Generative AI, LLMs, prompt engineering and RAG architectures Experience with LLMOps tools such as LangChain, LangSmith or LangGraph Strong understanding of model monitoring, validation and governance Excellent communication ...

practices across the function Design and deploy machine learning models across forecasting, customer segmentation, and propensity modelling Own end-to-end model development including feature engineering, validation, deployment, and monitoring Partner with Engineering and Product teams to build scalable, production-ready data science solutions Translate analytical outputs ...

offer . Job Description About the role: Join us as a Software Engineer in our critical Scam Prevention team. This is a high-impact engineering role with a strong product focus, dedicated to solving complex, real-world adversarial problems to directly protect our global customer base. How we work … securing customers' money, accelerating product growth by raising automation levels, and helping us engineer a world-class money transfer product. What do you need? Engineering Expertise: Professional experience with Java or other JVM-based languages, including the Spring framework. Experience building and designing scalable web applications that handle large ...

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 ...

About the Role We're recruiting on behalf of a major, well-established organisation for an MLOps Technical Manager to lead a growing ML engineering function. This is a genuinely interesting project: you'll work closely with data scientists, maintenance engineers, and cross-functional stakeholders to deliver scalable … from MLflow to AWS SageMaker, maintaining continuity and minimising disruption Define and enforce MLOps best practices across model training, serving, monitoring, and deployment MLOps & Engineering Design and maintain scalable ML infrastructure supporting batch and real-time environments Oversee the implementation of robust ETL/ELT pipelines, feature engineering ...

data to trading infrastructure, where speed and reliability are fundamental requirements Building integrated environments where quantitative researchers develop, test, and deploy predictive models - from feature engineering through execution Architecting cloud-based simulation and experimentation platforms that support large-scale quantitative research without becoming a bottleneck Standing up toolchains … need to understand how data, models, and trading systems interact. You can work productively with quants and understand what they need from the engineering side. You are senior enough to take ownership. CTO wants people he can hand large initiatives to and trust the output. 6 to 15+ years ...