AI Engineer - H&L

Aspire Software

The AI Automation Engineer is responsible for designing, deploying and improving AI agents and agentic workflows that automate routine operational work, augment teams, and improve speed, quality and scalability across the business.

This role is not about building disconnected experiments. It is about delivering real workflow change in priority areas, initially focused on Support, Sales, Professional Services and internal operational workflows. The role works closely with functional leaders and frontline teams to identify high value use cases, redesign workflows, deploy safe and useful agents, and ensure adoption in day-to-day operations.

The AI Automation Engineer helps shift repetitive, rules based and data intensive work to AI agents, while preserving human ownership of judgment, sensitive customer interactions, escalations, quality assurance, exception handling and continuous knowledge improvement.

What you will do

Design and deploy AI agents

• Design and deploy AI agents that support operational work, including support triage, internal workflow automation, sales support, onboarding tasks, knowledge handling, reporting and analysis
• Build agents and workflows that integrate with CRM, ERP, internal tools, SaaS platforms and APIs
• Use multi step workflows, tool using agents, decision logic and orchestration patterns where appropriate
• Move quickly from idea to pilot to production while maintaining clear standards for quality and reliability
• Design and implement AI agents and workflow automations that support PDLC activities such as requirements drafting, backlog preparation, technical research, code generation, test generation, documentation, defect triage, release support, and post release analysis
• Identify manual or repetitive PDLC tasks and convert them into scalable AI assisted workflows, including integrations with tools such as Jira, Confluence, GitHub, service platforms, and internal systems
• Partner with Product, Engineering, QA, Support, and other stakeholders to pilot, refine, and scale agentic PDLC workflows across teams

Redesign business workflows

• Identify high value automation opportunities with functional leaders and operators
• Redesign workflows so repetitive and rules-based work is handled by AI wherever safe and practical
• Support the shift to AI first intake, triage, knowledge retrieval, simple response handling and case preparation in service operations
• Help teams redesign work around a clear human and agent division of labour
• Help establish safe and scalable standards for AI use across the PDLC, including review points, escalation paths, auditability, fallback handling, and quality controls for AI generated artefacts

Governance, quality and evaluation

• Work within approved tooling standards, data and security guardrails, and design standards for agents and evaluation
• Establish monitoring, fallback handling, escalation logic, prompt and workflow controls, and human review mechanisms
• Test workflows with real users and real operational scenarios before scaling
• Measure agent quality, workflow success, containment, handoff quality and operational value
• Continuously improve workflows based on usage data, user feedback and service outcomes

Adoption and enablement

• Partner with functional leaders and frontline teams to support rollout and adoption
• Document new ways of working and help train users on how to work effectively with AI agents
• Surface workflow risks, resistance points and improvement opportunities
• Contribute reusable prompts, components, patterns and playbooks to the central AI capability

Cross functional delivery

• Collaborate with Product, Engineering, Support, Sales and Professional Services teams
• Help prioritise use cases based on measurable business value, feasibility and operational readiness
• Support the central AI capability with value tracking, reporting and continuous learning across initiatives

What success looks like

• Reduction in manual work and process cycle time
• Improved support response times and service leverage
• Workflow success rate and agent task completion quality
• Ticket containment or deflection where relevant
• Handoff quality for escalated or human reviewed work
• User adoption of AI enabled workflows
• Hours saved and team capacity created
• Contribution to service, productivity and customer outcomes
• Reduced manual effort across PDLC activities, faster movement from idea to release, improved quality of delivery artefacts, and stronger adoption of AI enabled workflows across Product and Engineering teams