AI Engineer - Platform

Build, enhance, and maintainAI-powered product featureswith a focus oncontinuous 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 throughtesting frameworks, automated checks, and model/feature validationpractices. This is a platform engineering and not infrastructure.

Key Responsibilities

• Build and enhance AI-driven features within existing products, prioritising incremental improvements and adoption.
• Develop and maintainagent tools, connectors, and integrations to enable AI workflows.
• Refine and optimisepromptsand interaction patterns for production use cases, including safety and reliability considerations.
• Supportexperiment tracking and model/version management(e.g., MLflow or equivalent).

Testing, Evaluation & Quality

• Build and maintain test functions for AI tools, including exploringagent-based testingpatterns (AI validating AI) where suitable.
• Develop evaluation frameworks for AI/agent performance (accuracy, robustness, regressions, hallucination controls as applicable).
• Implementquality gates, automated checks, and release readiness criteria for AI features.
• Supportchampion/challengervalidation approaches prior to promotion.
• Contribute tomodel observabilityand dashboards to track quality, performance, and usage patterns.
• Partner with Data Scientists and AI Engineers to product ionise research outcomes into reliable product capabilities.
• Coordinate with Platform/Dev Ops 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.

Skills required:

• 3+ yearsof 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 theML/AI lifecycle, including experimentation, evaluation, and release management.
• Experience with experiment tracking and model/version management concepts (tools such as MLflow are a plus).
• Strong communication skills with the ability to work effectively across technical and business stakeholders.

Preferred

• Hands-on experience withMLOps tooling(e.g., MLflow).
• Exposure toAI/agent frameworks(e.g., Lang Chain, Lang Smith, CrewAI, Azure AI Agent Service, AWS Strands or similar).
• Experience with
  
  GenAI application development, including prompt iteration and agent/tool patterns.
• Familiarity with cloud platforms (AWS/Azure) and/or data platforms (e.g.,Databricks).
• Working knowledge ofGitandCI/CDworkflows.
• Experience in financial servicesor other regulated environments.

Supporting Tools (Working Knowledge)

• Python ecosystem for AI/ML development (testing, packaging, experimentation)
• Git-based development workflows and code review practices

Observability/monitoring dashboards for AI feature performance (tooling varies by environment)