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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Gensler - **Location:** London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Autodesk Revit, Microsoft Azure, Big Data, Cloud Engineering, Code Review, Continuous Integration, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Visualization, Python (Programming Language), Machine Learning, Modular Design, Power BI, Azure Machine Learning, Software Engineering, Management of Software Versions, Feature Engineering, Azure Data Factory, Large Language Models, Data Lakes, Information Technology, Data Analytics, Machine Learning Operations, Software Version Control, Data Pipelines, Databricks - **Published:** August 28, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=f46b19e042119361 ## About the Role * Bachelor's or advanced degree in Computer Science, Software Engineering, Data Science, Statistics, Applied Mathematics, or a related field; equivalent production ML or data engineering experience considered. * 8+ years of hands-on experience building, deploying, and operating production ML systems on Azure, with practical MLOps and CI/CD experience including experiment tracking, deployment hygiene, monitoring, evaluation, and operational documentation. * Strong Python and software engineering fundamentals, including testing, version control, code review, reproducibility, modular design, documentation, and maintainable code practices. * Strong experience with Azure-based data and ML platforms, ideally including Azure ML, Databricks, Azure Data Factory or Fabric pipelines, Blob/Data Lake storage, and MLflow or similar tooling. * Experience building production-grade ETL/ELT pipelines and supporting ML workloads from ingestion through deployment. * Comfort moving between data engineering, machine learning, cloud architecture, infrastructure, and hands-on data exploration in a creative, collaborative environment. * Practical knowledge of containerization, infrastructure-as-code, and platform and tooling decisions in lean or fast-moving engineering environments. * Clear communicator who can explain technical decisions to non-engineering stakeholders, collaborate across disciplines, and mentor or support junior engineers. Meaningful pluses * Experience with AEC, real estate, BIM, geospatial, or digital twin data * Experience integrating ML into practitioner-facing workflows * Familiarity with responsible AI practices * LLM or GenAI deployment patterns * Power BI or other visualization tools * Experience with agentic engineering practices ## Description In the role of Machine Learning Engineer, you will drive and operationalize machine learning across AEC data domains including BIM, geospatial, design-performance, operational, and other project and practice data, translating technical capability into tools and workflows that practitioners can use. This is a digitally transformative, hands-on engineering role focused on building, deploying, maintaining, and optimizing machine learning systems and data pipelines, working with large-scale datasets to power production-ready intelligent systems and drive scalable outcomes across the firm. Success in this role means delivering reliable, secure, and well-governed ML systems that integrate into design workflows, are adopted by teams, and improve decision-making across the firm. You will help shape these capabilities inside an established Design Technology team, working alongside AI and data engineers, data scientists, designers, and product stakeholders to translate ambitious, pioneering ideas into production-ready platforms. What You Will Do * Design, build, and maintain reliable Azure-based ETL/ELT pipelines that deliver clean, documented data to models, APIs, internal applications, dashboards, and Gensler IP. * Own the production ML lifecycle on Azure - deployment, monitoring, versioning, retraining, rollback, and incident response - including model registries, feature stores, evaluation frameworks, and benchmarking. * Implement CI/CD, testing, reproducibility, and deployment standards for model and data workflows. * Extend and mature cloud data architecture and modeling strategies that support analytics and machine learning workloads. * Source, profile, and document datasets with business owners to confirm provenance, quality, fitness for use, and alignment with client-data and governance requirements. * Evaluate model performance and address root causes across data quality, feature engineering, training methodology, and architecture. * Translate research prototypes and experimental models into documented, production-ready systems. * Work across AEC data types such as BIM/Revit/IFC, geospatial, design-performance, occupancy, and other built-environment datasets. * Integrate machine learning outputs into design and delivery workflows so practitioners can access insights earlier and make more informed project decisions. * Apply responsible AI and data governance practices, including transparency, traceability, human oversight, and appropriate handling of client and project data. * Support the firm's data-driven design community through technical guidance, code review, and knowledge sharing. * Help define and track success criteria for deployed systems, including reliability, adoption, reuse, and measurable workflow or decision impact. ## Related Videos - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Enabling intelligent logistics automation: home-grown Industrial IoT platform at Austrian Post](https://www.wearedevelopers.com/videos/2018-enabling-intelligent-logistics-automation-home-grown-industrial-iot-platform-at-austrian-post) - [Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents](https://www.wearedevelopers.com/videos/1547-data-analytics-with-microsoft-fabric-end-to-end-use-case-with-data-agents) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)