AI Engineer

Purpose of the Role:

Agile Bridge is looking for an AI Engineer who can turn promising models into secure, scalable and dependable AI capabilities. You will work at the intersection of machine learning, deep learning and software engineering: designing model architectures, training and evaluating models, integrating them into software systems and improving their performance in production.

This role is model- and engineering-focused. A project may require you to select and adapt a neural-network architecture, develop a custom prediction or representation capability, benchmark a foundation model, optimise inference performance, or package a validated model behind a reliable service. You will be expected to consider the full production context: data quality, model behaviour, latency, throughput, resource use, cost, security and maintainability.

You will collaborate with Data Scientists on scientific validity and evaluation, Software and Applied AI Engineers on product integration, and Platform teams on deployment and observability. The role is distinct from a purely analytical Data Scientist position and from an Agentic AI role centred on flows, agents and business-process orchestration.

• Translate product and technical requirements into model objectives, architecture decisions and measurable acceptance criteria.
• Select, implement and train machine learning, deep learning, transformer or foundation-model approaches suited to the problem.
• Prepare training and evaluation data, including preprocessing, feature or representation engineering and dataset versioning.
• Run controlled experiments, tune models and evaluate accuracy, robustness, generalisation and computational efficiency.
• Package model-backed capabilities as maintainable services, APIs, libraries or platform components.
• Build or contribute to CI/CD and MLOps pipelines for testing, registration, release and environment promotion.
• Deploy and monitor AI workloads, responding to drift, latency, failures, resource constraints and cost concerns.
• Embed security, responsible AI, traceability and clear technical documentation throughout the model lifecycle.
• Research emerging methods and adopt them only where benchmarking demonstrates meaningful value.

• A bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics or a related quantitative field.
• Typically 3-5 years' relevant experience building and ope rationalising machine learning or deep learning capabilities.
• Strong Python capability and evidence of sound software-engineering practice.
• Hands-on experience developing, evaluating and improving machine learning or deep learning models.
• Experience integrating model-backed capabilities into a software or production environment.
• The judgement to balance model quality with latency, scalability, reliability, security, maintainability and cost.

We are looking for depth in the core engineering areas, not superficial exposure to every framework. Comparable tools are welcome where they demonstrate the same capability.

• Programming and software engineering:
  Strong Python, object-oriented design, testing, debugging, dependency management and clean, maintainable code. SQL is required; C# or Java is useful for integration-heavy environments.
• Machine learning and deep learning:
  Strong fundamentals across supervised and unsupervised learning, neural networks, loss functions, optimisation, regularisation and generalisation.
• Frameworks:
  Practical experience with PyTorch, Tensor Flow, scikit-learn or equivalent frameworks. Transformer or foundation-model experience should include evaluation and adaptation, not only API consumption.
• Model evaluation and optimisation:
  Experiment design, meaningful metrics, benchmarking, hyperparameter optimisation, error analysis, robustness testing and performance profiling.
• Data and storage:
  Data preprocessing, feature or representation engineering, dataset versioning, and experience with relational or non-relational databases.
• Integration:
  Building or consuming REST APIs, service contracts and model-serving…