Build, enhance, and maintain AI-powered product features with a focus on continuous product enhancement (CPE) and measurable end-user value. Engineer production-ready AI capabilities (agent tools, prompt optimisation, evaluation and observability) while collaborating closely with data science, AI engineering, and platform stakeholders. Contribute to quality-first delivery through testing frameworks, automated checks, and model/feature validation practices. This is a platform engineering and not infrastructure.

Job Duties

Key Responsibilities

AI Feature Development & Enhancement

1. Build and enhance AI-driven features within existing products, prioritising incremental improvements and adoption.
2. Develop and maintain agent tools , connectors, and integrations to enable AI workflows.
3. Refine and optimise prompts and interaction patterns for production use cases, including safety and reliability considerations.
4. Support experiment tracking and model/version management (e.g., MLflow or equivalent).

Testing, Evaluation & Quality

1. Build and maintain test functions for AI tools, including exploring agent-based testing patterns (AI validating AI) where suitable.
2. Develop evaluation frameworks for AI/agent performance (accuracy, robustness, regressions, hallucination controls as applicable).
3. Implement quality gates , automated checks, and release readiness criteria for AI features.
4. Support champion/challenger validation approaches prior to promotion.
5. Contribute to model observability and dashboards to track quality, performance, and usage patterns.

Collaboration & Delivery

1. Partner with Data Scientists and AI Engineers to productionise research outcomes into reliable product capabilities.
2. Coordinate with Platform/DevOps stakeholders for deployment needs and runtime requirements (without owning infrastructure delivery).
3. Support tactical AI solutions in response to emerging business requirements.
4. Participate in code reviews, documentation, and knowledge-sharing to strengthen team engineering standards.
5. Work within agile/team delivery practices and contribute to shared sprint and release goals.

Qualification

Master’s degree (or equivalent practical experience) in Engineering, Computer Science, Data/AI, or a related discipline.

Skills required

Essential

1. 3+ years of experience in ML/AI engineering with demonstrable experience delivering production features.
2. Strong proficiency in Python (designing, building, testing, and maintaining AI/ML applications).
3. Understanding of the ML/AI lifecycle, including experimentation, evaluation, and release management.
4. Experience with experiment tracking and model/version management concepts (tools such as MLflow are a plus).