AI Engineer

As an AI Engineer, this hands-on technical role builds, integrates and ships the AI services that power the organization's multi-year AI Workforce Transformation. Working under the direction of the Senior Manager, AI Engineering and the Principal AI Engineers, the role implements components of the AI platform – from orchestration workflows to LLM Gateway integrations – embedding AI into key workflows.

The position requires solid software-engineering fundamentals and a working knowledge of modern AI/ML tools. It exists to turn architectural designs and standards into reliable, production-ready AI capabilities that elevate operational productivity and augment human work, leveraging unique datasets.

Feature Development

Build and integrate AI services, APIs, pipelines and copilots to specification, writing clean, tested, production-ready code.

Platform Integration

Implement LLM Gateway and Model Context Protocol (MCP) integrations to connect AI services with enterprise systems (Jira, Confluence, core banking) under senior direction.

Data Preparation

Prepare, clean, label and version datasets and embeddings; apply PII handling and data-quality checks before data reaches models.

Automated Testing

Write and run automated tests for AI components – including edge cases, usability and reliability – and help maintain a robust log of test cases and results.

Monitoring & Observability

Instrument AI services with logging and metrics (Grafana, ELK) and monitor latency, throughput, error rates and model performance, raising alerts on anomalies.

Controls Execution

Apply security, privacy and responsible-AI controls (encryption, PII scrubbing, audit logging, bias checks) per standards, and escalate risks to senior engineers.

MLOps Support

Support MLOps / DevSecOps pipelines for model build, test, retraining and deployment, using infrastructure-as-code and integrated security scans.

Documentation

Document components, configurations and known limitations so that work is traceable and maintainable.

Code Review & Collaboration

Participate in code reviews, pair programming and knowledge sharing; make efficient use of source control and issue-tracking tools.

Continuous Improvement

Works well within an agile team, communicates clearly, and supports peers through reviews and knowledge sharing.

A Bachelor’s degree in Computer Science, Software Engineering or related field (a Master’s degree in AI/ML or Data Science is a plus), with 2–4 years’ software-engineering experience including hands-on exposure to AI/ML or data-intensive applications.

Software Engineering Fundamentals

Strong programming skills (e.g. Python, plus Java / Kotlin / C# or similar), a good grasp of data structures, APIs (REST/GraphQL) and version control (Git); writes clean, testable, maintainable code.

AI/ML Tools & LLMs

Working knowledge of AI/ML concepts and Large Language Models – prompt design, model APIs, embeddings, and vector databases (e.g. Pinecone, Weaviate, FAISS) for semantic search and retrieval.

Cloud & Containerisation

Practical experience deploying services on AWS and/or Azure using Docker; familiarity with Kubernetes and CI/CD pipelines (GitLab CI, Jenkins or GitHub Actions).

Data Handling & Privacy

Experience preparing, cleaning and versioning datasets; understanding of PII handling, dataquality checks and basic privacy/security practices for AI pipelines.

Testing & Observability

Skilled in automated testing (unit and integration, including edge cases) and instrumenting services with logging and metrics (e.g. ELK, Grafana).

Responsible AI Awareness

Awareness of explainability, bias and responsible-AI principles, with the willingness to apply controls and standards in day-to-day delivery.

Collaboration

Effective team player in an agile environment with good communication skills and the ability to manage competing priorities.

Certifications