1 to 25 of 27 Feature Engineering Jobs in London

realise the full potential of an AI enabled future. Key Responsibilities Build and ship production ready AI/ML features-from data ingestion and feature engineering to model training, evaluation, and deployment Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings … efficient memory/latency tuning Build and maintain MLOps/LLMOps workflows-CI/CD for models and prompts, model registry/versioning, feature stores, and automated promotion across environments Instrument observability for data, models, and prompts (telemetry, metrics, traces, dashboards, alerts); implement A/B tests and online ...

driven decision making across complex digital transformation programmes. Working with modern data platforms, high-quality datasets, and advanced AI frameworks, you will collaborate with engineering, product, and analytics teams to build models that are accurate, explainable, and scalable. The role empowers you to shape end-to-end analytical ecosystems … Collaborate with data engineers, analysts, product managers, and domain experts to translate business requirements into modelling tasks * Build end-to-end ML pipelines, including feature engineering, preprocessing, and deployment-ready outputs * Apply advanced techniques such as NLP, time series forecasting, anomaly detection, optimisation, or LLM/GenAI methods ...

complex digital transformation programmes. With access to modern data platforms, high quality datasets, and advanced statistical and AI frameworks, you will work closely with engineering, product, and analytics teams to build solutions that are accurate, explainable, and scalable. This role empowers you to shape end to end analytical ecosystems … data engineers, analysts, product managers, and domain experts to translate business requirements into well defined modelling tasks Build end to end ML pipelines-from feature engineering and preprocessing to deployment ready model outputs Apply advanced techniques such as NLP, time series forecasting, anomaly detection, optimisation, or LLM/ ...

complex digital transformation programmes. With access to modern data platforms, high-quality datasets, and advanced statistical and AI frameworks, you will work closely with engineering, product, and analytics teams to build solutions that are accurate, explainable, and scalable. This role empowers you to shape end-to-end analytical ecosystemsaccelerating … with data engineers, analysts, product managers, and domain experts to translate business requirements into well-defined modelling tasks. Build end-to-end ML pipelinesfrom feature engineering and preprocessing to deployment-ready model outputs. Apply advanced techniques such as NLP, time-series forecasting, anomaly detection, optimisation, or LLM/ ...

making across complex digital transformation programmes. With access to cutting edge AI frameworks, high performance computing environments, and modern data platforms, you will guide engineering and data science teams in building secure, scalable, and ethical AI systems. This role empowers you to shape end to end AI ecosystems-accelerating … more intelligent, AI enabled future. Key Responsibilities Define the enterprise AI architecture vision and reference patterns; align them to business goals, risk posture, and engineering standards across cloud and hybrid environments Design secure, scalable AI solutions covering data ingestion, feature engineering, model training, inference, and continuous feedback ...

making across complex digital transformation programmes. With access to cutting-edge AI frameworks, high-performance computing environments, and modern data platforms, you will guide engineering and data science teams in building secure, scalable, and ethical AI systems. This role empowers you to shape end-to-end AI ecosystemsaccelerating delivery … more intelligent, AI-enabled future. Your responsibilities: Define the enterprise AI architecture vision and reference patterns; align them to business goals, risk posture, and engineering standards across cloud and hybrid environments. Design secure, scalable AI solutions covering data ingestion, feature engineering, model training, inference, and continuous feedback ...

Bring: We are seeking a seasoned professional who is excited by the unique challenges of AI data. Must-Have Skills: Extensive Data Engineering Experience: Proven track record (3+ years) in designing, building, and maintaining large-scale data pipelines and data warehousing solutions. Cloud Platform Mastery: Expert-level proficiency with … Kafka, and data orchestration tools such as Apache Airflow or Prefect. ML Data Acumen: Solid understanding of data requirements for machine learning models, including feature engineering, data validation, and dataset versioning. Vector Database Experience: Practical experience working with vector databases (e.g., Pinecone, Milvus, Chroma) for embedding storage ...

Learning Engineer to design, build, and productionise machine learning models for our innovative matching platform. You will work across the entire ML lifecycle, from feature engineering to deployment automation, ensuring the optimisation and explainability of inference processes. Collaborating closely with data scientists and product teams, your role will … focus on enhancing MLOps practices, ensuring high standards of security, performance, and compliance. Responsibilities Build and maintain scalable feature pipelines within data lakehouse architectures. Develop fallback feature flows and implement robust data quality checks. Develop ranking, scoring, and entity-similarity models for the matching platform. Use modern ...

tangible mark on how people develop their careers. This is a role for someone who's done real AI work inside a high-calibre engineering organisation and worked closely with product. What you'll do Design and deploy LLM/RAG systems (GPT, Claude, Gemini) powering content generation, semantic … model in a notebook you've taken it to real users, measured it, and iterated. You've done this inside organisations that take engineering seriously. Whether that's a top-tier tech company, a defence/intelligence-grade AI startup, or a fast-scaling data-led business you know ...

defining how models should be evaluated, monitored and governed across their lifecycle. You will work closely with domain experts, the existing AI team, data engineering and product teams to ensure our AI systems behave consistently, accurately and responsibly. This is not an LLM-engineering or roadmap ownership role. … based systems. Standardise how experiments are structured, documented and compared across teams. Create reproducible workflows for testing prompts, model versions, embeddings and classical feature models. Ensure statistical soundness and methodological rigour in all experiments. You define “what goods like” in experimentation. Own the Evaluation Framework for Model Behaviour: Build ...

e.g., Mar's Simpel or similar) and improve performance and reliability Implement data modeling, transformation, and orchestration patterns aligned with best practices Apply data engineering fundamentals including partitioning, indexing, caching, cost optimization, and performance tuning Collaborate with upstream and downstream teams to ensure data quality, reliability, and SLAs Architecture … Evaluate tools and frameworks with a build vs. buy mindset, recommending pragmatic solutions Team & Delivery Responsibilities Act as a technical anchor for a data engineering team Provide technical guidance, code reviews, and mentoring to engineers Own delivery for assigned data products or pipelines — from design through deployment Collaborate with ...

conversational analytics products • Develop LLM-driven systems including fine-tuning and prompt pipelines • Apply statistical modelling and quantitative analysis to financial datasets • Conduct feature engineering, model evaluation, and experimentation • Work with engineering teams to integrate models into AWS-based platforms • Support development of AI systems used ...

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 ...

This is a senior individual contributor role with strong influence through mentorship, collaboration, and technical leadership. You will work closely with partners across product, engineering, and the business to design and deliver data‐driven solutions.We welcome candidates from a range of backgrounds and experiences who are excited to apply … Development** Lead by example through hands-on coding and model development. Design, prototype, and validate innovative solutions for high-impact problems. Partner with engineering teams to deploy models into production environments.* **Domain Expertise** Leverage deep knowledge of aviation, travel, or related industries to inform modeling approaches and deliver customer ...

including customers, sales teams and product teams Help customers develop an understanding of how behavioural AI models drive business outcomes Collaborate with product and engineering teams to identify new opportunities for data science innovation Contribute to the internal data science community and share best practices Skills & Experience Essential Strong … data science solutions that drive measurable business outcomes Experience building and evaluating machine learning models (e.g. clustering, NLP, deep learning) Experience with data exploration, feature engineering and model evaluation Familiarity with modern AI tooling and frameworks (e.g. agentic AI, MCP servers or similar AI infrastructure) Ability to work ...

investors and data-driven organisations. With data and AI at the core of their product, they are scaling quickly and investing heavily in their engineering and AI capabilities. You will join a collaborative, fully remote team that values transparency, curiosity and speed of execution. The Role You will … small, focused AI team and work closely with engineering to design, build and productionise new AI features across the platform. Key responsibilities include: Designing, training and deploying AI and ML models on complex, large-scale data. Building GenAI solutions using techniques such as RAG and frameworks like LangChain ...

BI. Secure: Implement rigorous data security, GDPR compliance, and client ring-fencing (Row-Level Security). Innovate: Create SQL and PySpark notebooks to prepare feature engineering datasets for AI/ML models. Collaborate: Work independently via Jira/GitHub, maintaining high CI/CD and documentation standards. What … Bring 5+ years of Data Engineering experience. Expertise: Deep knowledge of Snowflake and Microsoft Fabric . Tech Stack: Advanced SQL , Python , and PySpark (Notebooks). Modelling: Strong relational data modelling and cloud cost optimization skills. DevOps: Experience with GitHub, CI/CD, and workflow orchestration. Randstad Technologies is acting ...

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 ...

translate insights into business impact. Key Responsibilities Design and implement predictive and prescriptive models for MRO AI Solutions. Perform exploratory data analysis and feature engineering. Collaborate with Data Engineers to ensure data readiness for modeling. Communicate findings and recommendations to business stakeholders. Continuously improve models based on feedback ...

pipelines. • Contribute to proprietary data assistance products designed for analytical workflows. • Work across Python‐based modelling, experimentation, and error analysis. • Collaborate closely with engineering teams while leveraging an existing deployment function. • Support continuous improvement across modelling strategies, feature engineering, and evaluation methods. Your Skills and Experience • Strong ...

based MMM notebooks to incorporate additional media channels Prepare and validate input data from multiple sources prior to modelling Perform data cleaning, transformation, and feature engineering Run, calibrate, and sense-check MMM model outputs Support model validation and quality checks to ensure outputs are robust and usable Document ...

deploy production-grade ML models and automation systems that support critical business features.* Develop and maintain end-to-end ML pipelines, including data extraction, feature engineering, training, evaluation, deployment, monitoring, and automated feedback and retraining loops.* Architect scalable ML/AI components that improve performance and reliability across ...

modelling solutions Contribute to model documentation standards and lifecycle governance Own backlog contributions, user stories and acceptance criteria Drive alignment between business stakeholders and engineering team Skills & Competencies Strong knowledge of energy markets, including supply demand fundamentals, storage, flows and pricing Proven background in energy analytics and forecasting, including … scenario modelling Experience with major energy hubs (e.g., TTF, JKM) and cross-commodity linkage Solid understanding of feature engineering, statistical modelling, and ML workflows Strong Python skills (e.g., pandas, NumPy, Jupyter) Strong analytical thinking with the ability to simplify complex concepts If this sounds like a good ...

test hypotheses and inform product decisions. Define success metrics and measure the impact of released product changes. Platform & Data Enablement Work closely with Data Engineering to convert analytical prototypes into production ready pipelines, features and outputs. Ensure product enhancements are designed with data quality, lineage, monitoring and performance … internal and external use. Contribute to improving reuse of shared data assets and reducing duplication across products. Stakeholder Collaboration Work collaboratively with Product, Engineering, Insight and Commercial teams to ensure shared understanding of priorities and constraints. Clearly communicate analytical findings, recommendations and trade-offs to nontechnical stakeholders. Support product ...

Markets domains (counterparties, instruments, legal entities, regulatory taxonomies). Implement graph-RAG patterns for multi-hop reasoning and relationship-aware retrieval. 3. Vector Database Engineering — Architect and optimise the vector storage layer — embedding model selection, chunking strategies (semantic, hierarchical, sliding window), index tuning (HNSW, IVF-PQ), metadata filtering, hybrid … data governance, Delta Lake for reliable storage, Databricks Vector Search for managed embeddings, and Spark/SQL for large-scale data processing and feature engineering. 6. Query Routing & Orchestration — Implement intelligent query routing agents that decompose complex user questions into sub-queries, determine the optimal retrieval source (vector, graph ...