1 to 25 of 142 Permanent Feature Engineering Jobs in England

and translate them into scalable AI/ML and data-driven solutions. Key responsibilities: Design, develop, and deploy AI/ML models and solutions, including LLMs and GenAI. Perform feature engineering and selection to optimize model performance. Select and implement appropriate AI/ML algorithms, including supervised, unsupervised, and reinforcement learning models. Train, evaluate, and optimize models using More ❯

to manage model lifecycles. Leverage cloud platforms like Azure, AWS, and GCP for scalable ML model deployment. Employ frameworks like TensorFlow, PyTorch, and scikit-learn for model development. Data Engineering and Preparation Oversee data ingestion, cleaning, transformation, and feature engineering processes to ensure high-quality datasets. Work with large datasets and implement scalable data pipelines. Model Evaluation … and Optimization Evaluate model performance using metrics such as R2, RMSE, ROC-AUC, F1 score, and precision-recall. Optimize models through hyperparameter tuning, feature selection, and iterative testing. Collaboration and Deployment Partner with cross-functional teams to integrate ML solutions into business applications. Build and maintain APIs for deploying AI solutions at scale. Documentation and Best Practices Document all More ❯

Description Ciklum is looking for a Principal Data & AI Consultant to join our team full-time in London. We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology … that redefines industries and shapes the way people live. About the role: As a Principal Data & AI Consultant, become a part of a cross-functional development team engineering experiences of tomorrow. Responsibilities: Client engagements, account strategy & delivery responsibility within Data Science or AI Engineering domain(s) Leading client development or enhancement of their Data & AI strategy to align … with Business objectives Ability to support hands on development tasks where necessary/required Technical authority for solution design and development through client engagements Interacts with engineering teams and ensures that solutions meet customer requirements in terms of functionality, performance, availability, scalability, and reliability Senior Stakeholder management Collaborate on commercial development of account(s) with Account Manager & delivery team More ❯

an innovative team shaping next-generation solutions in the Telecoms sector. Key Responsibilities - Design, build and deploy AI/ML models, including LLMs and generative AI - Perform data exploration, feature engineering, and model optimisation - Apply supervised, unsupervised, and reinforcement learning algorithms - Conduct large-scale data analysis across structured and unstructured sources - Develop statistical and predictive models to drive More ❯

/ML team as we scale beyond our seed round. Key Responsibilities 1. Architecture & Hands-On Development Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (eg, neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem … learning models (eg, CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML, or equivalent). … Solid understanding of data-engineering concepts: SQL/noSQL, data pipelines (Airflow, Prefect, or similar), and batch/streaming frameworks (Spark, Kafka). Leadership & Communication: Proven ability to lead cross-functional teams in ambiguous startup settings. Exceptional written and verbal communication skills-able to explain complex concepts to both technical and non-technical stakeholders. Experience recruiting and mentoring engineers More ❯

/ML team as we scale beyond our seed round. ⸻ Key Responsibilities 1. Architecture & Hands-On Development • Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. • Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. • Productionize models in cloud/on-prem … learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. • Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). • Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). • Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML, or equivalent). … Solid understanding of data-engineering concepts: SQL/noSQL, data pipelines (Airflow, Prefect, or similar), and batch/streaming frameworks (Spark, Kafka). • Leadership & Communication: • Proven ability to lead cross-functional teams in ambiguous startup settings. • Exceptional written and verbal communication skills—able to explain complex concepts to both technical and non-technical stakeholders. • Experience recruiting and mentoring engineers More ❯

AI/ML team as we scale beyond our seed round. ⸻ Key Responsibilities Architecture & Hands-On Development Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem … learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML, or equivalent). … Solid understanding of data-engineering concepts: SQL/noSQL, data pipelines (Airflow, Prefect, or similar), and batch/streaming frameworks (Spark, Kafka). Leadership & Communication: Proven ability to lead cross-functional teams in ambiguous startup settings. Exceptional written and verbal communication skills—able to explain complex concepts to both technical and non-technical stakeholders. Experience recruiting and mentoring engineers More ❯

Aimpoint Digital is a premier analytics consulting firm with a mission to drive business value for clients through expertise in data strategy, data analytics, decision sciences, and data engineering and infrastructure. This position is within our decision sciences practice which focuses on delivering solutions via machine learning and statistical modelling. What you will do As a part of Aimpoint … Become a trusted advisor working with clients to design end-to-end analytical solutions Work independently to solve complex data science use-cases across various industries Design and develop feature engineering pipelines, build ML & AI infrastructure, deploy models, and orchestrate advanced analytical insights Write code in SQL, Python, and Spark following software engineering best practices Collaborate with … impact for your clients and do so through thoughtfulness, prioritization, and seeing a solution through from brainstorming to deployment. In particular you have these traits: Degree in Computer Science, Engineering, Mathematics, or equivalent experience. Experience with building high quality Data Science models to solve a client's business problems Experience with managing stakeholders and collaborating with customers Strong written More ❯

As a global leader in digital transformation, we are expanding our Data Practice across Europe to address growing client demand for advanced Data Science and Machine Learning (ML) engineering services. We are seeking a talented and experienced Principal Data Science & ML Engineering Consultant to join our dynamic team. This role emphasizes building scalable, production-ready ML solutions, optimizing … model performance and driving technical innovation across diverse industries. In this position, you will bridge the gap between data science and software engineering, delivering robust data-driven solutions that empower clients to solve real-world challenges and unlock measurable value. #LI-DNI Responsibilities Collaborate with clients to define their data science and ML strategies, ensuring alignment with business objectives … at various levels Contribute to client-facing activities, including presentations, workshops and responses to RFPs/RFIs Requirements Bachelors or Masters degree in Data Science, Statistics, Computer Science, Software Engineering or related fields. A Ph.D. is an advantage Extensive experience in data science, ML engineering or related roles. Experience in leading teams on projects in not required but More ❯

and technologies that you can then put into practice and become certified on various Cloud (and related) technologies that will help you to develop your own toolkit. Requirements Software Engineering: • Proficiency in programming languages used in ML, such as Python/Java. • Knowledge of software development best practices and methodologies. • Experience with version control systems (e.g., Git). • Familiarity … ML): • Deep understanding of machine learning principles, algorithms, and techniques. • Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. • Proficiency in data preprocessing, feature engineering, and model evaluation. • Knowledge of ML model deployment and serving strategies, including containerization and microservices. • Familiarity with ML lifecycle management, including versioning, tracking, and model monitoring. • Ability … as an MLOps Engineer or in a similar role, with an excellent understanding of AI/ML lifecycle management. Strong experience deploying and productionising ML models. Familiarity with data engineering concepts, including data pipelines, ETL processes, and big data technologies. Excellent problem-solving skills and the ability to troubleshoot complex issues in AI/ML systems. Technical Insight Skills More ❯

and technologies that you can then put into practice and become certified on various Cloud (and related) technologies that will help you to develop your own toolkit. Requirements Software Engineering: Proficiency in programming languages used in ML, such as Python/Java. Knowledge of software development best practices and methodologies. Experience with version control systems (e.g., Git). Familiarity … ML): Deep understanding of machine learning principles, algorithms, and techniques. Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. Proficiency in data preprocessing, feature engineering, and model evaluation. Knowledge of ML model deployment and serving strategies, including containerization and microservices. Familiarity with ML lifecycle management, including versioning, tracking, and model monitoring. Ability … as an MLOps Engineer or in a similar role, with an excellent understanding of AI/ML lifecycle management. Strong experience deploying and productionising ML models. Familiarity with data engineering concepts, including data pipelines, ETL processes, and big data technologies. Excellent problem-solving skills and the ability to troubleshoot complex issues in AI/ML systems. Technical Insight Skills More ❯

and technologies that you can then put into practice and become certified on various Cloud (and related) technologies that will help you to develop your own toolkit. Requirements Software Engineering: • Proficiency in programming languages used in ML, such as Python/Java. • Knowledge of software development best practices and methodologies. • Experience with version control systems (e.g., Git). • Familiarity … ML): • Deep understanding of machine learning principles, algorithms, and techniques. • Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. • Proficiency in data preprocessing, feature engineering, and model evaluation. • Knowledge of ML model deployment and serving strategies, including containerization and microservices. • Familiarity with ML lifecycle management, including versioning, tracking, and model monitoring. • Ability … as an MLOps Engineer or in a similar role, with an excellent understanding of AI/ML lifecycle management. • Strong experience deploying and productionizing ML models. • Familiarity with data engineering concepts, including data pipelines, ETL processes, and big data technologies. • Excellent problem-solving skills and the ability to troubleshoot complex issues in AI/ML systems. Technical Insight • Skills More ❯

developing and integrating cutting-edge AI solutions-including LLMs and AI agents -into our products and operations at a leading SaaS company. You'll collaborate closely with product and engineering teams to deliver innovative, high-impact systems that push the boundaries of AI in rebate management. This is a highly collaborative and fast-moving environment where your contributions will … or in collaboration with other specialists. Optimize model pipelines for latency, scalability, and cost-efficiency , and support real-time and batch inference needs. Collaborate with MLOps, DevOps, and data engineering teams to ensure reliable model deployment and system integration. Stay informed on current research and emerging tools in LLMs, generative AI, and autonomous agents , and evaluate their practical applicability. … Participate in roadmap planning, design reviews, and documentation to ensure robust and maintainable systems. Required Qualifications 5+ years of experience in machine learning engineering, applied AI, or related fields. Bachelor's or Master's degree in Computer Science, Machine Learning, Engineering , or a related technical discipline. Strong foundation in machine learning and data science fundamentals -including supervised/ More ❯

developing and integrating cutting-edge AI solutions—including LLMs and AI agents —into our products and operations at a leading SaaS company. You’ll collaborate closely with product and engineering teams to deliver innovative, high-impact systems that push the boundaries of AI in rebate management. This is a highly collaborative and fast-moving environment where your contributions will … or in collaboration with other specialists. Optimize model pipelines for latency, scalability, and cost-efficiency, and support real-time and batch inference needs. Collaborate with MLOps, DevOps, and data engineering teams to ensure reliable model deployment and system integration. Stay informed on current research and emerging tools in LLMs, generative AI, and autonomous agents, and evaluate their practical applicability. … Participate in roadmap planning, design reviews, and documentation to ensure robust and maintainable systems. Required Qualifications 5+ years of experience in machine learning engineering, applied AI, or related fields. Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or a related technical discipline. Strong foundation in machine learning and data science fundamentals—including supervised/ More ❯

features. Company Description Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change. By combining world-class engineering, industry expertise, and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and … expertise in Python-based AI/ML development, big data processing, and cloud-based AI platforms (Databricks, Azure ML, AWS SageMaker, GCP Vertex AI). Key Responsibilities Data Exploration & Feature Engineering Perform thorough Exploratory Data Analysis (EDA) and identify key variables, patterns, and anomalies. Engineer and select features for optimal model performance, leveraging domain understanding. Machine Learning & Statistical … skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know. Seniority level Mid-Senior level Employment type Full-time Job function Engineering and Information Technology Industries IT Services and IT Consulting #J-18808-Ljbffr More ❯

Build interactive front-ends (Tableau, Streamlit, Plotly Dash) so non-technical users can explore results intuitively Turn insights into actionable recommendations and present findings to clients; Contribute reusable components (feature engineering blocks, forecasting engines, GenAI pipelines) to our internal AI/ML toolkit Support business-development by shaping analytics in proposals and thought-leadership Tech you’ll use … to pick up new languages and frameworks quickly. What We’re Looking For Degree (2:1 or above) or Master’s in a quantitative field (Data Science, Computer Science, Engineering, Mathematics, Physics, Economics etc.) 0–2 yrs experience applying Python/SQL & core ML to real-world data sets Confidence explaining technical concepts to senior business audiences Intellectual curiosity … are treated with respect and dignity. L.E.K. Consulting is an Equal Opportunity Employer. Seniority level Seniority level Entry level Employment type Employment type Full-time Job function Job function Engineering and Information Technology Industries Business Consulting and Services Referrals increase your chances of interviewing at L.E.K. Consulting by 2x Get notified about new Data Scientist jobs in London, England More ❯

managers Design and deliver machine learning systems at scale that drive measurable impact for our business Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance Partner with stakeholders to propose innovative data products that leverage Trainline’s extensive datasets and state … and foster a culture of rigorous learning and experimentation We'd love to hear from you if you... Have a broad of understanding of data extraction, data manipulation and feature engineering techniques Are familiar with statistical methodologies Are skilled in one of reinforcement learning, predictive modelling (classification and regression) and have a solid understanding of recommendation systems Are … work at Trainline? Why not check us out on LinkedIn, Instagram and Glassdoor! Seniority level Seniority level Entry level Employment type Employment type Full-time Job function Job function Engineering and Information Technology Industries Technology, Information and Internet Referrals increase your chances of interviewing at Trainline by 2x Get notified about new Machine Learning Engineer jobs in London, England More ❯

with advancements in machine learning, AI, and data science methodologies to continuously improve processes and solutions Requirements for the role Master’s or Ph.D. in Computer Science, Statistics, Mathematics, Engineering, or a related field (Bachelor’s with significant experience also considered) 3+ years of experience in data science, machine learning, or a related role Proficiency in programming languages such … R, and libraries like TensorFlow, PyTorch, Scikit-learn, or Pandas Strong understanding of statistical modeling, probability, and optimization techniques Ability to work with messy, real-world datasets and perform feature engineering Excellent problem-solving skills and attention to detail Strong communication skills to convey complex concepts to diverse audiences Bonus points You’ve worked in an early-stage More ❯

foundation in machine learning algorithms, statistics, and data structures within relevant technical projects. Proficiency in programming languages, frameworks, and tools, such as Python, TensorFlow, PyTorch. Experience with data preprocessing, feature engineering, and model evaluation techniques. Highly Desirable: Experience working on aerospace-related projects Experience deploying MLOps solutions and working within CI/CD frameworks Experience with Linux systems More ❯

and requirements into tasks that can be solved with generative AI, machine learning, or other algorithmic solutions Process structured and unstructured data for generative AI use cases Collaborate with engineering and product development teams Following experimentation and model selection and validation, implement the solution using production-ready Python code Contribute to the full software development lifecycle, including requirements gathering … colleagues and stakeholders Present information using data visualization techniques Qualifications/Requirements: A master’s degree (or equivalent) in a numerate discipline such as Statistics, Machine Learning, Computer Science, Engineering, or Physics. A doctorate is a plus but not required Mid-level professional with 2-4 years of ML/AI experience, typically at an Analyst level role or … external equivalent A scientific approach to solving problems, an analytical mind, and experience in applied machine learning and generative AI Experience in applying LLMs to analyze text documents, prompt engineering, and manual and automatic evaluation of LLM outputs Good foundational knowledge of standard machine learning algorithms and statistical methods Proficient in python and its standard data science and ML More ❯

and deliver NLP based machine learning systems at scale that drive measurable impact for our business Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance Partner with stakeholders to propose innovative data products that leverage Trainline's extensive datasets and state … practices and platforms like ML Flow Have experience with agile delivery methodologies and CI/CD processes and tools Have a broad understanding of data extraction, data manipulation and feature engineering techniques Are familiar with statistical methodologies. Have good communication skills Nice to have Experience with LangGraph or LangChain Experience with transport industry and/or geographical information More ❯

Machine Learning R&D Engineer (KTP Associate). As a Machine Learning R&D Engineer Associate, you will develop Machine Learning (ML) solutions that will innovate and transform key engineering activities in Buro Happold. You will work closely with Buro Happold's computational team to build reusable AI/ML datasets, develop and optimise ML models, facilitate their deployment … within the company, and co-define data-driven solutions that have the potential to revolutionise the Architecture, Engineering and Construction (AEC) industry. This role presents an exciting opportunity to work in collaboration with leading academics at Birmingham City University, to apply knowledge and technical innovation, delivered on site at the company. The Machine Learning R&D Engineer (KTP Associate … construction projects (recent award-winning projects include Battersea Power Station in London and the K64 Keflavik Airport Area Masterplan). Buro Happold covers several domains of expertise, including structural engineering, façade design, MEP engineering, and many others. Buro Happold has a long history of developing and applying innovative computational solutions, like the open-source framework Buildings and Habitats More ❯

with different machine learning techniques and algorithms, including supervised, unsupervised, semi-supervised, reinforcement, and deep learning; Design and optimize machine learning pipelines and workflows, incorporating techniques for data cleaning, feature engineering, model selection, and hyperparameter tuning; and Develop scalable and efficient machine learning infrastructure and systems for training, testing, and deploying models in production environments. Qualifications Bachelor's More ❯

develop scalable Python-based systems that support ML-driven risk classification, tagging, and approval triage Integrate ML models into microservices and APIs for use within AI Judge workflows Lead engineering design reviews, establish coding standards, and ensure system robustness and security Build and maintain feature pipelines and model-serving infrastructure using cloud-native tools Work closely with ML … scientists, data engineers, and product managers to align on requirements and delivery timelines Drive engineering quality, CI/CD integration, observability, and unit testing for AI-enabled software components Mentor junior engineers and uphold engineering excellence across the team Required Qualifications, Capabilities, and Skills: Master's degree in computer science, Software Engineering, or related field 6+ years … AI/ML software engineer Proficiency in Python with deep experience in building distributed and containerized services (e.g., Flask/FastAPI, Docker, Kubernetes) Strong understanding of ML deployment workflows, feature engineering, and serving architectures Experience building and deploying APIs and ML inference services in production Familiarity with ML model management, versioning, and performance monitoring Strong engineering fundamentals More ❯

Proficient: • Languages/Framework: Python, Fast API, Azure UI Search API (React) • Cloud: Azure Cloud Basics (Azure DevOps) • GitLab: GitLab Pipeline • Ansible and REX: Rex Deployment • Data Science : Prompt Engineering + Modern Testing • Data pipeline development and monitoring • Understanding of AI/ML algorithms and their applications • MLOps frameworks • Knowledge of cloud platforms (Azure ML especially) • Model deployment process … Storage Account, PostgreSQL, Cosmos o Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) o Knowledge of cloud platforms (AWS SageMaker, Google AI Platform) o Expertise in data pre-processing, feature engineering, and model evaluation o Understanding of software engineering principles (version control, CI/CD, containerization) o Familiarity with distributed computing and big data tools (Spark, Hadoop More ❯