Full job description 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
*Build and enhance AI-driven features within existing products, prioritising incremental improvements and adoption.
*Develop and maintain agent tools , connectors, and integrations to enable AI workflows.
*Refine and optimise prompts and interaction patterns for production use cases, including safety and reliability considerations.
*Support experiment tracking and model/version management (e.g., MLflow or equivalent).
Testing, Evaluation & Quality
*Build and maintain test functions for AI tools, including exploring agent-based testing patterns (AI validating AI) where suitable.
*Develop evaluation frameworks for AI/agent performance (accuracy, robustness, regressions, hallucination controls as applicable).
*Implement quality gates , automated checks, and release readiness criteria for AI features.
*Support champion/challenger validation approaches prior to promotion.
*Contribute to model observability and dashboards to track quality, performance, and usage patterns.
Collaboration & Delivery
*Partner with Data Scientists and AI Engineers to productionise research outcomes into reliable product capabilities.
*Coordinate with Platform/DevOps stakeholders for deployment needs and runtime requirements (without owning infrastructure delivery).
*Support tactical AI solutions in response to emerging business requirements.
*Participate in code reviews, documentation, and knowledge-sharing to strengthen team engineering standards.
*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
*3+ years of experience in ML/AI engineering with demonstrable experience delivering production features.
*Strong proficiency in Python (designing, building, testing, and maintaining AI/ML applications).
*Understanding of the ML/AI lifecycle, including experimentation, evaluation, and release management.
*Experience with experiment tracking and model/version management concepts (tools such as MLflow are a plus).