Machine Learning & AI Engineer

Wednesday 11 March 2026 Hybrid Working Machine Learning & AI Engineering – Grade D

This role is based in the Lloyds Technology Centre (LTC) and operates as a service provider to the UK business team. While the activities and responsibilities of the role holder can be assigned managed (i.e day to day task direction) as the business team requires, the role holder remains an LTC colleague, and their Line Management remains in LTC.
Job Profile Summary

As a specialist in artificial intelligence and machine learning, knowledge of key software development approaches, Python testing frameworks, CI/CD, and source control contributes to developing end-to-end machine learning systems in Python. You will productionise machine learning pipelines that implement data science solutions to the bank’s data problems, ensuring they are scalable and efficient. You’ll take ownership for developing and delivering features, being deeply hands-on throughout the process while also learning and growing within the team.

• Python (Programming Language) - Ability to write clean, efficient, and maintainable code using Python. Skilled in libraries such as NumPy, Pandas, and Scikit-learn for data manipulation and modelling. Familiar with object-oriented programming and debugging practices.
• Machine Learning - Knowledge of core ML concepts including supervised and unsupervised learning, model evaluation, and optimisation. Experience with frameworks like TensorFlow or PyTorch and techniques such as feature selection and hyper-parameter tuning.
• Software Engineering - Understanding of software development principles, version control (Git), and modular coding. Able to contribute to codebases and follow established patterns.
• Data Structures and Algorithms - Familiarity with common structures (arrays, lists, trees) and basic algorithms (sorting, searching). Can apply them in coding tasks.
• Google Cloud Platform - Basic knowledge of GCP services such as BigQuery, Cloud Storage, and AI Platform. Able to deploy simple workloads and follow documentation.
• Feature Engineering - Ability to create features from raw data using transformations, handling missing values, and improving model performance.
• Structured Query Language (SQL) - Capable of writing queries for data retrieval and filtering, including joins and aggregations.
• Data Engineering - Exposure to ETL processes and data pipelines. Understands concepts of data ingestion and transformation. Familiar with data build tool (dbt) and terraform.
• DevOps - Familiarity with CI/CD concepts and tools like Jenkins or GitHub Actions. Able to follow deployment pipelines.
• Solution & Technical Architecture - Basic understanding of system components and integration points. Awareness of scalability and design principles.
• Deep Learning Theory & Applications - Knowledge of neural network basics and common architectures (CNN, RNN). Able to experiment with pre-built models.
• Applying Statistics - Understanding of descriptive statistics, distributions, and hypothesis testing. Can apply basic statistical methods in analysis.

• Artificial Intelligence Ethics - Awareness of ethical considerations in AI such as bias, transparency, and fairness. Understands organisational principles.
• Machine Learning Risk & Governance - Knowledge of risks associated with ML models, including bias and drift. Awareness of governance frameworks.
• Data Ethics - Understanding of privacy, consent, and fairness in data use. Applies organisational guidelines.

• Technical Product Management - Understanding of product lifecycle and requirements gathering. Able to assist in translating technical needs into the work.
• Business & Commercial Insight - Ability to interpret important metrics and align technical work with business objectives.
• Managing Key Relationships - Maintains positive working relationships with stakeholders. providing regular communications to manage expectations.