Senior/Lead Machine Learning Engineer

Qualifications

• Required Skills & Experience
• Nice to Have

About the role

Design, build, and deploy machine learning solutions that solve real business problems, moving from prototype to production.

Responsibilities

• Apply traditional ML (e.g., regression/classification/clustering) and deep learning techniques where appropriate, selecting models based on evidence and constraints.
• Demonstrate strong ML fundamentals, including the mathematics behind models (probability, statistics, optimisation, linear algebra), and explain trade-offs clearly.
• Develop and deploy ML and data science solutions from proof of concept to production
• Perform data exploration, feature engineering, and model development on large datasets
• Track experiments, metrics, and model versions (e.g. MLflow)
• Collaborate with data engineers and AI engineers to integrate models into platforms
• Continuously improve models based on performance, feedback, and data drift

Requirements

• 5+ years in applied machine learning and deep learning roles
• Strong grounding in core ML concepts and their mathematical basis:
• Probability & statistics, hypothesis testing, bias/variance, regularisation
• Optimisation (e.g., gradient-based methods), loss functions, evaluation metrics
• Linear algebra fundamentals used in ML (vectors/matrices, decompositions at a practical level)
• Solid practical experience with traditional ML modelling (feature engineering, model selection, validation, and error analysis).
• Demonstrable exposure to deep learning (architectures, training dynamics, evaluation), beyond surface-level familiarity.
• Proven ability to build good quality software, not just models—clean code, testing, debugging, and maintainable design.
• Strong programming skills (typically Python; additional languages a plus) and experience integrating ML into production systems.
• A clear problem-solving mindset: structured thought process, ability to reason through ambiguous requirements, and iterate effectively.
• Hands-on experience delivering ML solutions end-to-end, including prototyping, validation, and production/operations.
• Experience with Databricks and Spark
• Hands-on use of MLflow or similar model lifecycle and MLOps frameworks
• Experience with deep learning frameworks (e.g. PyTorch)
• Practical experience with GenAI / LLMs
• Exposure to AWS Bedrock & AWS SageMaker
• Strong SQL and data analysis skills