Applied AI Software Engineer

Role Summary The Applied AI Software Engineer will be responsible for the rapid technical design and delivery of AI agents and frameworks built on top of SIMPRO FSMs. Working closely with the AI Platform Product Management team, they will be the key technical owner transforming commercial opportunities into production-grade AI capabilities. This role demands an aggressive entrepreneurial spirit; they must be scrappy, able to figure out complex integration challenges on the fly, and driven by a "speed to deploy" mentality. While speed is critical, they must also maintain high standards of quality, ensuring tight collaboration with SIMPRO’s product and R&D teams to create seamless, monetizable integrations.

Key Responsibilities

• Partnership-Driven Development: Design and implement scalable agentic models and AI agents specifically tailored to support the workflows and commercial goals defined in the roadmap.
• Rapid Prototyping & Integration: Lead the engineering effort to integrate AI models into the SIMPRO core platform with velocity, building on top of APIs and microservices that ensure high performance and interoperability.
• Scalable MLOps Architecture: Develop and maintain robust agentic frameworks ensuring the infrastructure can scale to meet the demands of various strategic partners.
• Data Strategy & Collaboration: Collaborate closely with data teams to define requirements, manage feature stores, and ensure the quality of data flowing between the core SIMPRO platform and new AI agents.

Required Qualifications

• "Scrappy" Problem Solver: Demonstrated ability to work in a chaotic, fast-moving environment where they must figure things out as you go; prioritize solutions over process when necessary to meet aggressive deadlines.
• 15+ years of experience in a technical role, with at least 2+ years focused specifically on developing and deploying production AI/agentic systems.
• Bachelor’s degree in Computer Science, Engineering, AI, Machine Learning, or a related quantitative field is highly preferred.
• Proficiency in modern programming languages and ML frameworks suited for production environments (focus on architectural patterns rather than specific tool syntax).
• Practical experience with cloud-based MLOps tools and infrastructure for deploying models at scale.
• Strong grasp of computer science fundamentals, data structures, and algorithms.
• Experience with the specific challenges of real-time conversational AI or LLM integration is a strong plus.