Mott Macdonald
A global engineering, management, and development consultancy delivering innovative solutions.
Data Scientist
Lead AI/ML development integrating generative AI and agentic workflows for engineering projects.
Job Highlights
About the Role
The firm helps clients place information at the core of their operations, delivering outcome‑focused, game‑changing results by combining engineering legacy with cutting‑edge technology. By connecting innovation to outcomes, they enable better planning, delivery, and operation of client asset bases. • Lead end‑to‑end development and deployment of AI/ML projects that address real engineering and consultancy challenges. • Design and implement agentic AI architectures (e.g., LangChain, LangGraph) to automate complex reasoning across technical documentation and project management. • Build rapid proof‑of‑concepts and transition them into high‑availability, enterprise‑grade applications hosted on Azure. • Collaborate with structural engineers, environmental consultants, and digital architects to embed AI/ML into the broader ecosystem. • Scale AI solutions with cost‑optimized deployment (LlmOps) while ensuring ethical and responsible AI practices.
Key Responsibilities
- ▸ai development
- ▸agentic ai
- ▸proof‑of‑concept
- ▸azure deployment
- ▸llmops scaling
- ▸ethical ai
What You Bring
• 5+ years of NLP experience and 2+ years with large language models; fine‑tune and deploy LLMs (GPT, Llama 3, Falcon) via Azure AI Foundry. • Hands‑on expertise in agentic AI orchestration using LangChain, Crew AI, or Llama Index; mastery of Retrieval‑Augmented Generation, RAG Fusion, and chain‑of‑thought prompting. • Strong foundation in Transformers, BERT, GPT, NLU, and NLG principles. • 5+ years of machine‑learning experience with algorithms such as XGBoost, Random Forest, advanced regression, and time‑series models. • Proficient in Python, clean‑code practices, design patterns, and scalable architecture; experience with end‑to‑end enterprise software development. • Practical experience with deep‑learning frameworks like PyTorch or Keras. • Demonstrated ability to deploy models in Azure ML, manage the full ML lifecycle, and work with vector (Pinecone, Milvus, PgVector) and graph databases. • Knowledge of LlmOps, Docker, and Kubernetes to ensure model reliability and cost efficiency. • Collaborative mindset, translating AI concepts into clear business value for non‑technical stakeholders. • Strong communication and leadership skills, comfortable taking additional challenges and working in fast‑paced Agile environments.
Requirements
- ▸nlp
- ▸llms
- ▸langchain
- ▸python
- ▸azure ml
- ▸docker
Benefits
The company supports flexible working arrangements and offers a benefits package that includes agile working, critical illness and compassionate leave, paternity leave, group life and medical insurance, career mobility, global employment opportunities, and global collaboration and knowledge sharing. • Benefits: agile working, critical illness and compassionate leave, paternity leave, group term life and medical insurance, career mobility options, short‑ and long‑term global employment opportunities, global collaboration and knowledge sharing.
Work Environment
Hybrid