Machine Learning Engineer - AI Team (Global Digital)
- Model Development & Integration
- Develop and fine-tune machine learning models, particularly in NLP, computer vision, and generative AI in collaboration with developers and data analysts as well as with 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
- Data Pipelines & Experimentation
- Build end-to-end pipelines for data collection, preprocessing, 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
- Collaboration & Innovation
- 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 and 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 operationalizing models via REST APIs (e.g., using 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)
- Development & Collaboration
- Comfortable building and maintaining ML pipelines from prototype to production
- Familiarity with tools for experiment tracking and version control (e.g., MLflow, Git, W&B)
- 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
- Work alongside passionate, creative individuals who lead their industry, transform ideas into reality and celebrate the beauty of human connection
- Enjoy various benefits, such as hybrid working and gym membership discounts
- Enjoy the opportunity to attend events at Populous-designed venues
- Connect and learn at regular social and knowledge sharing events including an annual conference and a weekend abroad Christmas party
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