AI Engineer

• Build AI features end to end, including models or pipelines, production services, and product interfaces
• Develop forecasting and rate-recommendation logic for the auto-publish engine, including tolerance guardrails
• Build LLM-driven features such as natural-language rate explanations, conversational property-performance queries, and automated market commentary
• Develop rate-intelligence pipelines by ingesting competitor rates, cleaning third-party data, and generating reliable model signals
• Create evaluation harnesses and monitoring to detect model degradation
• Build full-stack functionality including APIs, data models, and user interfaces
• Take technical ownership of ambiguous problems, make trade-off decisions, and remain accountable for production results

AI Developer – AMP

• Design and deploy AI agents supporting support triage, internal workflow automation, sales support, onboarding, knowledge handling, reporting and analysis
• Build agents and workflows integrating with CRM, ERP, internal tools, SaaS platforms and APIs
• Use multi-step workflows, tool-using agents, decision logic and orchestration patterns
• Move ideas rapidly from pilot to production while maintaining quality and reliability standards
• Design AI agents and workflow automations supporting PDLC activities including requirements drafting, backlog preparation, technical research, code generation, test generation, documentation, defect triage, release support and post-release analysis
• Convert repetitive PDLC tasks into scalable AI-assisted workflows using Jira, Confluence, GitHub, service platforms and internal systems
• Partner with Product, Engineering, QA, Support and other stakeholders to pilot, refine and scale workflows
• Identify and redesign high-value automation opportunities with functional leaders and operators
• Establish human-agent divisions of labor, review points, escalation paths, auditability, fallback handling and quality controls
• Work within approved tooling, data, security and agent design standards
• Establish monitoring, fallback handling, escalation logic, prompt and workflow controls and human review mechanisms
• Test workflows with real users and operational scenarios before scaling
• Measure agent quality, workflow success, containment, handoff quality and operational value
• Improve workflows using usage data, user feedback and service outcomes
• Support rollout and adoption, document new ways of working and train users
• Contribute reusable prompts, components, patterns and playbooks to the central AI capability
• Collaborate across Product, Engineering, Support, Sales and Professional Services
• Prioritize use cases based on measurable business value, feasibility and operational readiness
• Support value tracking, reporting and continuous learning across initiatives