51 to 75 of 111 Feature Engineering Jobs in London

implement robust evaluation frameworks to systematically assess and measure the performance of AI agents across key operational metrics. Practice advanced prompt and context engineering (e.g., Chain-of-Thought, ReAct, function calling/tool-use prompts), implement output validation and guardrails to reduce hallucinations. Deploy scalable AI services to cloud … LangChain, CrewAI, AutoGen, LangGraph, ADK). Experience with generative models (transformers, GANs/VAEs; diffusion models a plus). Strong understanding of data preprocessing, feature engineering, and evaluation techniques. Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). Strong communication skills for both technical ...

that enable machine learning at scale.The Lead MLOps Engineer will design, build, and maintain the infrastructure that supports the full machine learning lifecycle, from feature generation and experimentation through to deployment, monitoring, and continuous improvement. You will play a key role in ensuring our predictive models and data products … platform architecture, helping shape the tooling, standards, and operating model used to deliver machine learning across the organisation.Develop and maintain automated workflows for feature engineering, model training, testing, deployment, monitoring, and retraining.Partner with Data Scientists and Data Engineers, and ML Engineers to operationalise predictive models and data products.Establish ...

making embedded inside the teams instead of wasting time in wider forums, to reduce time to impact. You bring deep, hands-on AI/engineering expertise and the judgement to turn ambiguous technical problems into shipped, production-grade systems, while mentoring the engineers around you. Key Responsibilities This role … seeking professionals with the following required skills and qualifications to help us achieve our goals: Strong Experience of full-stack or AI/ML engineering experience, including production deployments. Strong proficiency in Python and at least one of TypeScript/JavaScript, Go, or similar. Demonstrated ability to take ideas ...

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. Qualifications We are fully aware that it is uncommon for a candidate to have … 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 Experience working with relational and non-relational databases ...

enable machine learning at scale. The Lead MLOps Engineer will design, build, and maintain the infrastructure that supports the full machine learning lifecycle, from feature generation and experimentation through to deployment, monitoring, and continuous improvement. You will play a key role in ensuring our predictive models and data products … platform architecture, helping shape the tooling, standards, and operating model used to deliver machine learning across the organisation. Develop and maintain automated workflows for feature engineering, model training, testing, deployment, monitoring, and retraining. Partner with Data Scientists and Data Engineers, and ML Engineers to operationalise predictive models ...

decisions, improve customer outcomes, and build scalable data science capabilities for a growing product area. You will collaborate closely with cross-functional teams, including engineering, product, analytics, operations and risk management. Key Responsibilities: Lead the development and deployment of machine learning models and data science solutions to improve Receive … state-of-the-art capabilities in the Spend domain A bit about you: Proven track record of deploying models from scratch, including data preprocessing, feature engineering, model selection, evaluation, and monitoring Solid knowledge of Python, and ability to make and justify design decisions in your code. You know ...

will work hand in hand with the automation business and tech teams across R&D and manufacturing. You bring deep, hands-on AI/engineering expertise in Physical AI and the judgement to turn ambiguous technical problems into shipped, production-grade systems, while mentoring the engineers around you. … first production versions directly for use cases where the path is clear - before the Sr. Solutions Engineer takes them forward to scale. AI-Native Engineering Operate AI agent workflows (planning, coding, testing, review, documentation) for autonomous prototype delivery - selecting the right level of automation for each engagement. ...

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 ...

agent tools and APIs to connect agents with external services, databases, and internal systems, ensuring robust output parsing and error handling. Practice advanced prompt engineering and implement output validation and guardrails to reduce hallucinations. Design microservices-based architectures and orchestrate multi-step workflows; instrument agents for tracing, metrics … with agent frameworks such as LangChain, CrewAI, AutoGen, LangGraph. Knowledge of generative models including transformers, GANs, VAEs, and diffusion models. Understanding of data preprocessing, feature engineering, and model/agent evaluation techniques. Familiarity with cloud platforms and containerization technologies. Strong problem‐solving skills and ability to work independently ...

payments and banking operations. Job Responsibilities: Deliver machine learning and AI solutions for payments and banking operations, from discovery to production rollout Apply agentic engineering practices to build LLM-powered workflows and evaluate their quality, safety, and reliability Contribute to deployment workflows including containerization, CI/CD, automated testing … consumers Partner with product, operations, risk and control, and technology teams to clarify requirements and deliver data-led improvements Build reusable components such as feature engineering pipelines, evaluation harnesses, and orchestration patterns Participate in code and design reviews; contribute to best practices, documentation, and team standards Communicate with ...

drive better customer and commercial outcomes. You'll work across: Customer segmentation, propensity modelling and churn prediction. Revenue and demand forecasting. Python-based modelling, feature engineering and model validation. Production deployment, cloud infrastructure and MLOps. Translating complex analysis into actionable commercial insights. Collaborating with product, engineering ...

Design and develop predictive, machine learning, and Generative AI solutions. Build prototypes, proof-of-concepts, and production-ready analytical models. Perform data exploration, feature engineering, model development, and validation. Collaborate with data engineers and architects to operationalise solutions. Ensure solutions follow Responsible AI, governance, and security best practices. ...

culture of innovation and continuous learning. Required Qualifications, Capabilities, And Skills Advanced degree in a STEM field (Degree in Computer Science or Software Engineering), with experience in AI/ML. Proven track record of AI/ML model development and deployment of AI/ML applications in a production … mainly as cloud service APIs (e.g., OpenAI). Experience with integrating user feedback to establish self-improving AI applications. Solid understanding of data preprocessing, feature engineering, and model evaluation techniques. Familiarity with cloud platforms (AWS). Excellent problem-solving skills and the ability to work independently and collaboratively. ...

business objectives Building prototypes and proof-of-concepts using Databricks and modern data platforms Translating business requirements into technical product specifications Designing data structures, feature sets, scoring methodologies, and model outputs Evaluating emerging AI technologies and analytical techniques for commercial application Supporting the launch and performance optimisation of data … predictive models, machine learning solutions, or advanced analytics products Ability to work with large-scale transactional, behavioural, or marketing datasets Understanding of data modelling, feature engineering, and analytical best practices Experience using Databricks or similar modern analytics platforms Knowledge of cloud-based data environments, ideally within Azure ecosystems ...

machine learning platforms. Data governance and metadata tooling to improve transparency, trust, and reuse across the organisation. Your Experience 5+ years experience in data engineering, building and maintaining scalable data platforms. Strong SQL and data modelling skills, with experience designing analytical datasets. Experience with modern data stack tools (e.g. … e.g. Cube.js) or metrics‐layer design. Exposure to data governance and metadata tools (e.g. OpenMetadata, Hive Metastore). Experience supporting machine learning workflows and feature engineering pipelines. Why This Role Matters This platform is central to how we enable trusted, scalable, and reusable data across sportsbook. It provides ...

customer engagement, and operational efficiency. Partner with stakeholders to define problem statements, success metrics, data requirements, and practical implementation plans. Conduct data exploration and feature engineering to uncover drivers of outcomes and improve model performance and interpretability. Develop robust evaluation frameworks, including appropriate baselines, validation strategies, monitoring metrics … model performance reporting. Support deployment of models into production in collaboration with Engineering, contributing to reproducible pipelines and model documentation. Monitor models in production, identify performance drift, propose improvements, and support ongoing recalibration or retraining where required. Apply probability and statistical inference to design experiments, interpret results, and provide ...

security, identity and compliance (Entra ID, RBAC, network isolation, data protection). Strong experience designing LLM‐based solutions, including Retrieval‐Augmented Generation (RAG), prompt engineering strategies and agent‐based architectures, leveraging frameworks such as Semantic Kernel, LangChain, and Microsoft Agent Framework. Deep practical experience with classical Machine Learning, including … supervised and unsupervised learning, feature engineering, model selection, training pipelines, evaluation frameworks and production lifecycle management, equivalent in depth to GenAI expertise. Proven ability to assess client use cases and make well‐reasoned architectural decisions between classical ML and Generative AI approaches; and articulate clearly why. Strong experience ...

constrained decisioning pipelines running in production. Requirements You have expert-level Python skills for end-to-end data science and production machine learning, from feature engineering to inference. You possess advanced SQL capabilities for querying complex customer, transaction, campaign, margin, and measurement datasets. You bring extensive hands … contact budgets across audiences. Build, deploy, and scale robust production ML pipelines on Databricks using Unity Catalog, automated workflows, and scheduled scoring jobs. Establish engineering rigor across the team through structured Git workflows, pull request reviews, automated testing, and controlled release cycles in Azure DevOps. Implement ...

platform design.* Ensure architectures meet security, privacy, and regulatory requirements, including GDPR and data residency considerations.* Review solution designs, provide architectural assurance, and support engineering teams through design authority and peer review.* Design data architectures that enable AI adoption — ensuring data is discoverable, well-governed, and AI-ready … machine learning, GenAI, and agentic use cases across the business.* Partner with AI/ML teams to define patterns for feature engineering, model data pipelines, vector search, and the safe exposure of enterprise data to AI systems.* Evaluate emerging technologies and make pragmatic build/buy/adopt ...

aligned with its parent group Responsible AI standards. This is a senior individual-contributor role with broad influence. You will partner closely with Engineering Leads, Product Managers, Data Scientists, and data teams within our client. Key Responsibilities: Define and own the AI architecture vision for Protégé in PatentSight+, ensuring … driven enrichment and classification of patent data at scale, ensuring results are dependable, auditable, and consistent with established IP metrics. Establish prompt engineering standards, evaluation harnesses, and quality frameworks to govern LLM behaviour and maintain output accuracy in production. Partner with Product Management to assess technical feasibility and shape ...

degree in a quantitative field; 3+ years of SQL proficiency; and 2+ years of experience in data modeling, ETL development, data warehousing, and software engineering best practices (version control, CI/CD, object-oriented programming). Statistical & Machine Learning Modeling: 2+ years of hands-on experience in statistical analysis … Experience working with Databricks, Snowflake or similar cloud platforms for end-to-end machine learning projects, including collaborative notebook development, experiment tracking with MLflow, feature engineering at scale, job orchestration and pipeline automation, dashboarding for stakeholder reporting, and production model deployment. Experience working with AWS big data processing ...

Bring Hands‐on experience in data science or applied machine learning. Strong understanding of Generative AI, LLMs, and RAG principles. Experience with data mapping, feature engineering, and end‐to‐end ML lifecycle management. Proficiency across Microsoft's Azure AI and data ecosystem, with an eagerness to adopt Fabric ...

observability/monitoring, tracing, guardrails, safety controls, and reliability practices Build and integrate model endpoints into products and workflows (APIs/services), partnering with engineering through to deployment. Work across cloud platforms (Azure/AWS/GCP) integrating storage, compute, orchestration, and model/runtime components. Assess data readiness … modelling/RAG (fitness, quality, access) and define remediation requirements Collaborate in cross-functional squads (DS/DE/engineering/product) and contribute to reusable assets and ways of working. Communicate clearly with stakeholders on trade-offs, evaluation results, risks, and adoption actions. Own end-to-end workstream ...

forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. Own the full model lifecycle from problem framing, data exploration, feature engineering, back-testing, validation, deployment, monitoring, tuning, and incident response when models degrade or fail in production. Partner closely with engineering, data … Strong hands‐on technical skills, with the ability to work directly with data, code, ML frameworks, and complex datasets, while partnering effectively with software engineering and platform teams. Experience building scalable, reliable, and explainable AI/ML solutions that meet operational, governance, and regulatory requirements and deliver measurable business ...

experimental output computationally actionable. Relocation? Yes Compensation £50k-£70k, depending on experience, plus equity & benefits Location London Reports To Saif Ur-Rehman (Director, Data Engineering) Responsibilities Multi-omic data analysis and interpretation. You own the computational analysis of datasets generated by our wet lab (metagenomics, metabolomics, 16S), from … feature extraction through statistical analysis and biological interpretation. Computational studies. You run your own proof-of-concept investigations: microbiome-compound interactions, community dynamics, predictive biomarkers. You're a scientist with hypotheses, not a technician with a task list. Perturbation data quality. You own the validation layer between the bench ...