16 of 16 Permanent Feature Engineering Jobs in England

deliver production-grade ML systems for a leading digital gaming and gambling platform. This is a hands-on role combining data science expertise with engineering skills - you'll build models, optimise algorithms, and deploy solutions at scale to enhance customer engagement and decisioning. You'll work closely with Data … science & modelling: Develop, validate, and optimise predictive models using advanced ML algorithms (e.g., gradient boosting, logistic regression, ensemble methods). End-to-end ML engineering: Deploy models as APIs, batch jobs, and streaming services; implement CI/CD, monitoring, and rollback strategies. Feature engineering & pipelines: Build scalable ...

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 ...

Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic … Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong experience with Appian low-code platform including ...

machine learning and deep learning models. Work on AI/ML projects including NLP, computer vision, recommendation systems, and generative AI. Perform data cleaning, feature engineering, and exploratory data analysis (EDA). Build and manage data pipelines and model training workflows. Deploy models into production and monitor performance. … Collaborate with Product, Engineering, and Business teams to translate business problems into AI solutions. Conduct model evaluation, A/B testing, and performance tuning. Document models, experiments, and technical processes. Required Skills & Qualifications Classic Machine learning (Regression, predictive Analysis, Classification, Clustering) Machine learning Model Optimisation Strong proficiency in Python ...

clients. As a senior leader, you will interact frequently with customers, provide expert opinions, and contribute to HCL’s strategic vision. Key Responsibilities Technical & Engineering Leadership • Design and implement AI and cloud architectures, integrating GenAI technologies to enhance functionality and scalability. • Lead architectural discussions with clients, providing expert guidance … Skills & Experience • Deep knowledge of machine learning operations (MLOps) and experience in deploying, monitoring, and maintaining AI models in production environments. • Proficiency in data engineering for AI, including data preprocessing, feature engineering, and pipeline creation. • Expertise in AI model fine-tuning and evaluation, with a focus ...

Partner closely with data scientists to turn experimental models into well-engineered, production-ready systems Develop and maintain end-to-end ML pipelines, including feature engineering, training, evaluation, versioning, monitoring, and automated retraining Contribute to MLOps capabilities, including CI/CD pipelines, model registries, infrastructure automation, and containerised … regulatory requirements Advise business and product teams on opportunities for automation, predictive modelling, and GenAI enablement Contribute to reusable ML components, internal frameworks, and engineering best practices Stay up to date with emerging AI trends, tooling, and foundation models to inform strategic decision-making About You You will ...

models for user personalisation, including recommendation systems, ranking models, user segmentation, and content understanding, with a strong focus on TensorFlow-based development. Data Pipeline Engineering: Build and maintain scalable data pipelines to support feature engineering and model training across large structured and unstructured datasets, leveraging cloud‐native … relevance. Experimentation: Lead A/B testing and offline experimentation to evaluate model performance and guide ongoing improvement. Cross‐Functional Collaboration: Work closely with engineering, product, data, and research teams to ensure ML solutions align with product and business goals. Research & Innovation: Stay informed on advances in machine learning ...

operational use, including synthetic data generation. Build and productionise ML/NLP solutions for legal and business use cases to enable better decision making. Feature Engineering & Integration : Develop new features, integrate data from internal/external sources, and create unified views to enhance performance and outcomes. Pipeline Development ...

cloud and on-prem data platforms , automation, and emerging AI-driven capabilities, and is specifically suited to candidates with strong, hands-on data engineering experience , particularly in building, operating, and optimising complex data pipelines, data models, and integration workflows at scale. Role Overview and Responsibilities As a Data & Automation … delivery processes using technologies such as Azure Data Factory, Databricks, SSIS, and SQL . Reducing manual interventions through automation and standardisation , including data preparation, feature engineering, and training-data pipelines. Preparing data models and datasets (DWH/Lakehouse) to support business intelligence, analytics, and operational reporting. Monitoring ...

things that go into production, and wants to learn from experienced engineers in a fast-moving environment. You’ll be part of a modern engineering function working on large-scale data and AI products, contributing to model development, data pipelines, and deployment workflows that power mission-critical platforms. … Build, train, and evaluate machine learning models using Python 📊 Work with large, complex datasets to support AI-driven products and insights 🧠 Assist with feature engineering, model experimentation, and performance optimisation ☁️ Contribute to data and ML pipelines in cloud-based environments 🗄 Work with structured and semi-structured data using ...

skills, and a results-driven mindset Role responsibilities: Build, tune, and maintain predictive and ML models using AWS SageMaker Analyse large datasets and perform feature engineering to improve model performance Run experiments, test hypotheses, and optimise models for accuracy and value Monitor model performance and manage retraining over ...

multi-tenant SaaS platform. Create ETL pipelines for large, complex property datasets. Monitor, tune, and iterate on model performance. Collaborate with product and engineering teams on ML-driven features. Document solutions and communicate clearly with technical and non-technical stakeholders. Key Skills 4+ years as a Machine Learning Engineer … similar role. Strong Pythonengineering skills. Experience deploying ML models in production. Understanding of multi-tenant SaaS architecture. Solid ETL, data processing, and feature engineering experience. Strong statistical and analytical capabilities. Experience in start-up or scale-up environments. Desirable MLOps tools (MLflow, Kubeflow, Sagemaker, etc.) Experience in PropTech ...

Familiarity with cloud-native (GCP/Azure/AWS), Kubernetes, API-first integration, and observability stacks (metrics, logs, traces) Experience in ML/AI (feature engineering, MLOps, model monitoring), automation frameworks (RPA/BPM/runbooks), and security/compliance. Proven leadership in large-scale digital transformation, operating ...

proof-of-concept development using Databricks notebooks, analytical outputs and machine learning techniques to validate product concepts Own technical product definition, including data structures, feature sets, scoring methodologies, model architectures and delivery formats in collaboration with the Product Team Define and monitor technical success metrics (data coverage, refresh latency … needs into technical product requirements Support rapid prototyping and proof-of-concept development using Databricks notebooks and analytics outputs Define product-level data structures, feature sets, scoring outputs and delivery formats in collaboration with the Product Team Product Launch & Performance responsibilities: Define technical success metrics (data coverage, refresh latency ...

large language models, agentic AI – across research, rankings, submissions and product innovation. Oversee the end-to-end analytics lifecycle, from data acquisition and feature engineering through to model development, validation, deployment and monitoring Ensure AI-driven outputs are explainable, transparent and aligned with Chambers’ standards and reputation ...

model lifecycle. This isn't a traditional "build and hand off" role, you'll manage everything from data collection to feature engineering, model development, testing, and deployment. Key Responsibilities: Develop credit risk predictive models, including scorecards, income models, and early repayment models. Build automation for lending decisions … Create forecasting and marketing models to support customer segmentation and growth strategies. Own the full modelling process, from raw data through deployment, without separate engineering teams. Collaborate with cross-functional teams to ensure models are robust, actionable, and scalable. Continuously improve modelling approaches as volumes and portfolios expand. REQUIREMENTS ...