1 to 25 of 52 Feature Engineering Jobs in London

Consulting Firms. Our Solutions & AI Labs (SAIL) practice is looking for an experienced Senior Manager to lead consultancy engagements and grow our AI & Solutions Engineering capability. The Forward Deployed AI & Solutions Engineer role will lead the delivery of AI-enabled solutions embedded in our clients’ teams, combining deep technical … solutions directly embedded into business processes. We support clients across a number of industries and offer deep expertise in AI/ML, Cloud, Platform Engineering, and Managed Solutions. What you will be doing As a Senior Manager in SAIL, you will own and lead consultancy engagements ...

winning the work, through to delivery governance and team leadership. You will bring SME-level depth in at least one of AI/ML Engineering, Platform Engineering, or Software Engineering, and apply it to create real, lasting value for our clients. Although we do not expect … Specialism You will bring expert-level knowledge in at least one of the following domains, and working knowledge across the others: Agentic AI & LLM Engineering Design and build production-grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering ...

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. AI/ML Engineering Direction Leads the definition and execution of AI/ML engineering direction across ...

Staff AI Engineer London | Engineering | In office | Full-time The Role We’re looking for a Staff AI Engineer who thrives at the intersection of software engineering, data and AI. This role is ideal for engineers who have experience building real, lovable products leveraging state … techniques and modern technologies. You’ll own the full lifecycle of AI applications: building data pipelines, prompt & feature engineering, integrating models into high throughput, low latency services, and deploying production‐ready, AI‐powered features that delight our 70+ million users. What You’ll Own Build, deploy, and maintain ...

Experience designing and analysing A/B tests, defining success metrics, understanding selection bias, and evaluating ranking systems using both online and offline methodologies. Feature Engineering & Applied Machine Learning : Strong background in feature engineering, tabular machine learning, calibrated scoring systems, and production use of models such … Entity‐Aware Ranking : Ability to incorporate relationships between companies, sponsors, lenders, law firms, instruments, documents, and events into ranking and recommendation decisions. Production ML Engineering : Experience building feature pipelines, online and batch inference systems, low‐latency ranking services, experimentation platforms, and monitoring for production machine learning systems. Strong ...

solution offerings. Provide expertise and guidance on AI best practices, contributing to the organization's AI strategy and innovation efforts. Conduct data analysis and feature engineering to prepare data for use in AI models, utilizing Azure Data Lake and other data storage solutions. Develop robust testing and validation … services. Defines security procedures to be followed, and delegates tasks at the appropriate level. Skills & Experience Required: University degree in computer science or software engineering and/or 5+ years equivalent work experience within a cloud environment. Strong Programming Skills: Proficiency in programming languages such as Python, C# ...

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 ...

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 ...

bridge the gap between machine learning, data science, and engineering. What you’ll do Shape the architecture of data products built for: analytics forecasting feature engineering customer behavior insights integration of new data sources Data Transformation & Governance Lead best practices in: data modelling performance optimization governance consistent reliable … Explore open-source tools Engage with the broader tech community Help keep Prima at the cutting edge What we’re looking for Software & Data Engineering Strong expertise in Python Passion for writing: clean reliable production-grade code Distributed Data Processing Experience building and operating: batch processing systems distributed data ...

experimenting with new estimators, models, and features to boost payment performance across our merchants portfolio. You will work closely with Data Scientists, Product, and Engineering to enhance our core offering, protect customer lifetime value through network intelligence, and ensure safe model launches through robust observability. Key Responsibilities Contribute … other Data Scientists and engineers to productionise ML features, models, and evolve our evaluation and monitoring frameworks. Write high‐quality, interpretable Python code for feature engineering and model training, contributing directly to our core products. Communicate hypotheses, evaluation results, and monitoring dashboards clearly to both technical ...

emerging AI technologies and recommend practical applications across the organisation.### Data Science & Analytics* Perform advanced statistical analysis and modelling across large-scale operational and engineering datasets.* Develop feature engineering, experimentation and model evaluation frameworks.* Create insights and recommendations that drive decision making for senior stakeholders.* Build … maintain reproducible data science workflows and pipelines.### Product Delivery* Partner with engineering and product teams to transition solutions from proof-of-concept into production.* Define success metrics and measurement frameworks for AI products.* Support model monitoring, governance and continuous improvement activities.* Contribute to AI product roadmaps and prioritisation discussions.### ...

technical leadership across the full lifecycle of machine learning systems powering a new matching platform. This role is accountable for defining ML architecture, establishing engineering standards, driving MLOps maturity, and ensuring that our models are scalable, secure, explainable, and governed to enterprise‐grade standards. You will contribute … compliance framework. This is a highly hands‐on, highly technical, principal‐level role that combines architectural vision with deep practical expertise in ML engineering and AWS-native MLOps. Key Responsibilities Technical Leadership & Architecture Define the end‐to‐end ML architecture for the matching platform, including data pipelines, model training ...

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 ...

Work With Building and deploying AI and Machine Learning solutions Designing and implementing end-to-end AI and ML pipelines, from data acquisition and feature engineering through to deployment and optimisation Developing scalable ML pipelines and engineering frameworks AI model training, evaluation, optimisation and deployment Cloud-native … stakeholders to understand business challenges and translate requirements into AI solutions Working closely with technical and non-technical teams throughout programme delivery Cross-functional engineering, architecture and data teams Agile software development and delivery methodologies Exploring and adopting emerging AI technologies and approaches to drive innovation and business value ...

. Job Description About the role: Join us as a Senior Software Engineer in our critical Account Integrity 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 ...

such as GANs and VAEs. Proven experience applying these techniques to real-world tasks (text, code, image, multimodal generation) and familiarity with advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands‐on exposure to API-based LLM providers … project management skills, overseeing timelines, milestones, and deliverables; coordinating internal teams and clients to ensure project success. Data Understanding : Foundational grasp of data preprocessing, feature engineering, and data quality assurance processes to support AI model requirements. Responsible AI & Governance : Awareness of AI governance, safety, and compliance considerations—data ...

Your Impact Customer Onboarding & Engine Customization: Lead the technical deployment of our engines for new clients. Partner closely with customers, Customer Success, Product, and Engineering to adapt and customize ML models, ensuring they accurately reflect client-specific business logic, operational guardrails, and unique data nuances. Analytic Engine Development: Design … implement robust Machine Learning (LightGBM, XGBoost) and Deep Learning (PyTorch) models specifically focused on complex demand sensing and resolving cold-start forecasting problems. Feature Engineering Pipelines: Build and optimize scalable data pipelines leveraging a hybrid SQL/Python architecture (dbt, Snowflake) to extract rich, localized temporal signals from ...

distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems. Mentor junior team members and utilise model explainability methods to drive feature performance improvements. About You 5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale business problems. Deep … statistics, plus supervised and unsupervised modelling techniques. Proven experience modelling complex interactions (e.g., cluster or network effects). Expertise in model explainability to tune feature engineering and understand feature interactions. Ability to write high-quality, production-grade Python code and collaborate effectively with non-technical stakeholders. Proficient ...

full delivery lifecycle, from early ideas through to production systems that create measurable impact for our customers and the business. As MLOps Engineering Manager, you will help shape how we build, deploy and operate machine learning products at scale, working closely with ML Engineers, Data Engineers, Software Engineers, Data … operation of machine learning products, ensuring models and AI systems are production‐ready, observable and able to support Trainline’s growth. Partner closely with engineering, data science, product and data teams to bring strong engineering standards into machine learning delivery, while recognising the specific challenges of data ...

solution development—from data exploration and experimentation through to deployment, monitoring, and continuous optimisation. They will work closely with cross-functional teams including data engineering, infrastructure, DevOps, and product management to translate business needs into robust, reusable machine learning architectures. Key Responsibilities Design and deploy large-scale machine learning … systems into production using modern engineering practices and tools. Build and maintain core ML infrastructure, including pipelines for feature engineering, model training, evaluation, deployment, and monitoring. Automate the full AI/ML lifecycle, covering data ingestion, experimentation, tuning, and visualisation. Collaborate with product teams to convert business ...

allow ML teams to move from research to production quickly, safely, and cost‐efficiently. You’ll partner closely with Applied Science, Data, Product, and Engineering leadership to build a scalable, secure, and observable ML platform that powers multiple business lines. Your impact will come not only from the systems … growing ML and GenAI adoption across multiple teams, products, and business lines. Lead the architecture of platform capabilities across the full ML lifecycle: experimentation, feature engineering, artifact management, training, deployment, monitoring, retraining, and governance. Design cloud-native infrastructure for distributed training and inference, including GPU-based environments, autoscaling ...

value creation initiatives to be backed by reliable, real-time data. When companies partner with JMAN, we combine our data science and data engineering expertise with our deep commercial understanding to deliver tangible, high-value outcomes at pace. Founded in 2010, JMAN has a global footprint with offices … Data Science or applied machine learning, ideally in consulting, high‐growth, or commercial environments. Strong applied machine learning expertise, including: Model selection and development Feature engineering Evaluation and validation Deployment into production environments Demonstrated experience taking models beyond experimentation into operational, business‐critical systems. Strong background in statistics ...

Bedrock. What you’ll do Partner with stakeholders across the business to explore high‐impact opportunities. Own the full lifecycle: problem framing, data discovery, feature engineering, modelling, evaluation, deployment, monitoring, and iteration. Build and productionize LLM features where appropriate (retrieval‐augmented generation, evaluation, safety guardrails, cost/latency … Contribute to DS/ML standards: experimentation, model governance, documentation, and reproducibility. Mentor junior scientists, work with external contractors and collaborate closely with data engineering on pipelines and data quality. What you’ll bring A proven track record delivering projects in a Data Science or AI. Experience deploying models ...

London Rate: Inside IR35 Duration: Initial 6 months What you'll be doing Deliver end-to-end data science solutions, from problem definition and feature engineering through to deployment and monitoring. Apply machine learning, NLP and GenAI techniques to customer interaction data, including text classification, summarisation, embeddings ...

play a key role in designing, developing and deploying AI and Machine Learning solutions, working across the full ML lifecycle from data preparation and feature engineering through to model deployment and optimisation. Working with Python, SQL and leading ML frameworks, you'll build scalable pipelines, develop and evaluate ...