Machine Learning Engineer - AI Team (Global Digital)

Populous; City of Westminster; On-site; GBP 60,000 - 80,000; Full time

Job summary

A global design and architecture firm is seeking a Machine Learning Engineer to develop AI capabilities for life-changing tools. Collaborating in an agile team, you'll work across the full ML lifecycle and integrate models into production systems. The ideal candidate should have strong Python and ML skills, and experience with cloud environments. You'll be tasked with building pipelines, monitoring model performance, and contributing to innovative projects that impact the built environment.

Benefits

Dynamic and collaborative work environment

Opportunity for impactful work in AI

Professional development opportunities

Qualifications

• Several years of experience in machine learning engineering or applied ML roles.
• Strong Python programming skills with familiarity with ML libraries.
• Experience integrating machine learning models into workflows and applications.

Responsibilities

• Develop and fine tune machine learning models, particularly in NLP, computer vision and generative AI.
• Collaborate with developers to embed ML capabilities in user facing applications.
• Build end-to-end pipelines for data collection, pre-processing, feature engineering and training.

Skills

Machine Learning

Python programming

NLP (Natural Language Processing)

Generative AI

Cloud platforms (AWS, Azure, GCP)

API development

Education

Experience in machine learning engineering or applied ML roles

Tools

PyTorch

TensorFlow

MLflow

Weights & Biases

Job description

This role sits within our AI Technology Team. As a Machine Learning Engineer, you'll help bring powerful AI capabilities to life-shaping tools that support staff across Populous. You'll work across the full ML lifecycle, from data prep and model experimentation to deployment and ongoing optimisation. You'll collaborate closely with full stack developers and our AI Lead to prototype, fine tune and integrate machine learning models -particularly in natural language processing (NLP), generative AI and semantic search-into production systems that drive better outcomes in the built environment.

Responsibilities

• Develop and fine tune machine learning models, particularly in NLP, computer vision and generative AI in collaboration with developers, data analysts and design team members.
• Adapt and integrate foundation models (e.g. Anthropic, OpenAI, Cohere) for targeted use cases.
• Implement and maintain APIs for inference, batch jobs and model access within production systems.
• Collaborate with developers to embed ML capabilities in user facing applications.
• Build end-to-end pipelines for data collection, pre-processing, feature engineering and training.
• Work with structured, unstructured and spatial data across a variety of formats and sources.
• Manage model evaluations, experiment tracking and dataset versioning with reproducibility in mind.
• Monitor model performance and detect drift or degradation over time.

Tooling & Infrastructure

• Use ML frameworks such as PyTorch, TensorFlow and Hugging Face Transformers.
• Operate within cloud platforms (AWS, Azure or GCP) for model training and deployment.
• Leverage tools like MLflow, Weights & Biases or LangChain for model tracking and orchestration.
• Contribute to the design and iteration of internal AI/ML powered design and productivity tools.

Collaborative Environment

Work in agile, cross functional teams alongside designers and domain experts.

Stay current on research, tooling and trends in AI/ML - bringing new ideas into practice.

Help shape our AI architecture and share your perspective in technical planning and team discussions.

Core Technical Skills

• Several years of experience in machine learning engineering or applied ML roles.
• Strong Python programming skills with familiarity with ML libraries (e.g. scikit learn, PyTorch, TensorFlow).
• Experience integrating machine learning models into workflows and applications.
• Solid understanding of vector search and embedding based systems (e.g. FAISS, Pinecone, Weaviate).
• Comfortable operationalising models via REST APIs (e.g. FastAPI or Flask).
• Proficient in handling both structured and unstructured data (text, images, spatial data).
• Experience working in cloud based environments (AWS, Azure or GCP).

Experience Building Pipelines

• Familiarity with tools for experiment tracking and version control (e.g. MLflow, Git or W&B).

Communication & Teamwork

Strong communication skills- able to explain technical decisions to non technical collaborators.

Effective working independently or as part of an interdisciplinary team.

Mindset & Domain Interest

• Research oriented and self motivated, with a desire to apply AI in tangible, impactful ways.
• Interest in the built environment - whether through urban design, spatial data or large scale civic infrastructure.
• Background in architecture, engineering, construction or location aware applications is a bonus, not a requirement.

Preferred (but Not Required)

• Experience with LLM orchestration frameworks (e.g. LangChain, Haystack).
• Familiarity with retrieval augmented generation (RAG), prompt tuning or hybrid search architectures.
• Exposure to MLOps workflows or orchestration tools (e.g. Airflow, Argo).
• Understanding of AI governance topics such as data privacy, fairness and explainability.
• Experience building internal tooling, design assistants or custom AI interfaces for non technical users.

Opportunity & Culture

We're seeking an applied machine learning engineer who loves solving real world problems with data and AI. You'll thrive here if you're hands on, curious and excited to bring new capabilities into tools that shape spaces and human experience.