AI Automation & Agent Builder

Aspire Software

The AI Automation & Agent Builder Engineer is an AI-first builder responsible for designing, building, and shipping intelligent agents and automated workflows that replace manual work and generate measurable business value. This role is not a research or experimentation role. AI Automation Engineers are embedded directly inside portfolio company teams, understanding real operations, and deploying agents that handle real work customer service, sales outreach, onboarding, document processing, and more.

Some of what you build will replace manual workflows within Aspire s portfolio companies. Some of it will become new revenue-generating products sold to their customer bases. Either way, it ships to real users and is measured by real impact. This role is foundational in AI-native organizations and is often the highest-ROI AI position in the company.

Core Responsibilities

• Build Internal AI Agent Teams
• Design and deploy AI agents that handle operational work across portfolio companies including customer support (Tier 1 & 2), SDR and sales outreach, onboarding automation, content workflows, and engineering maintenance
• Automate core business processes: CRM updates, document processing, reporting, and analytics pipelines
• Work directly with portfolio company operators to identify the highest-value automation opportunities
• Measure impact in hours of labor replaced, team output increased, or cost savings generated

Build External AI Products

• Develop new AI-powered products such as vertical voice agents, AI copilots, workflow automation tools, and AI documentation systems tailored to vertical markets
• Own the product from prototype through launch, including reaching first paying customers
• Integrate AI capabilities with existing systems: CRM, ERP, internal tools, SaaS platforms, and third-party APIs
• Build agentic workflows including multi-agent coordination, decision engines, and tool-using AI agents

Ship and Iterate

• Move fast from idea to MVP to production bias toward working software over documentation
• Run beta tests with real users, gather feedback, and improve continuously
• Optimize AI agent performance: latency, cost, accuracy, and human intervention rate
• Ensure production reliability through monitoring, evaluation, and model lifecycle management
• Collaborate cross-functionally with product, engineering, and go-to-market teams

What This Role Is Not

• Not a research or proof-of-concept role everything you build ships to real users
• Not a traditional backend or systems engineering role focused on ticket execution
• Not bound by story points, sprint rituals, or rigid SDLC stages
• Not limited to internal tooling what you build may become a product customers pay for

Technical Skills

• Hands-on experience with LLM integrations, RAG architecture, and prompt engineering
• Proficiency with workflow orchestration and automation platforms (e.g. n8n, Zapier, Make, LangChain, CrewAI)
• Ability to build and maintain API integrations across CRM, ERP, and SaaS systems
• Understanding of vector databases, embedding pipelines, and model hosting
• Strong debugging and problem-solving skills across full AI system stacks

Mindset & Approach

• Outcome-oriented: you measure success in labor hours saved, revenue generated, and adoption rate not tickets closed
• Strong operational intuition ability to understand a business workflow and identify where AI creates the most leverage
• Comfortable working directly with business operators, not just engineering teams
• Ability to reason about AI system limitations, failure modes, and appropriate human-in-the-loop design

Key Performance Indicators (KPIs)

Automation Impact

• Number of business processes automated
• Hours of manual labor saved per month
• Reduction in manual task volume across portfolio companies

Agent Performance

• Agent task success rate
• Agent accuracy and hallucination rate
• Human intervention rate (lower = better)

Efficiency & Business Value

• Cost savings generated through automation
• Response time improvements across automated workflows
• Operational throughput increase

Delivery & Adoption

• Number of AI agents and automations shipped per quarter
• Time from idea to production deployment
• Number of teams actively using AI agents built by this role
• Internal workflow adoption rate