Principal Software Engineer (AI) (CAD)

Team Summary:

The role sits within the AI Engineering & Enablement team, which accelerates AI adoption across the PEO by building shared platforms, establishing best practices, and enabling teams to embed AI and data capabilities into their products and workflows.

Job Summary:

The Principal AI Engineer is a senior technical leader responsible for driving PointClickCare’s AI first and data first transformation across products and platforms. This role defines and delivers production grade AI systems, including agentic and data driven solutions—that are secure, observable, scalable, and aligned with healthcare regulatory requirements. 

Beyond hands on technical leadership, the Principal AI Engineer acts as a coach and mentor to engineering teams, elevating AI and data engineering maturity across the organization. This role partners closely with Product, Engineering, Architecture, Advanced

Technology, and SaaS Operations to identify high value opportunities for AI driven innovation and to transition prototypes into reliable, measurable business outcomes. 

Key responsibilities:

Lead the reference architecture for AI and agentic systems within key product domains, defining standards and reusable patterns for reasoning loops and orchestration, tool/function calling, memory and state management, data integration and retrieval, safety, governance and interoperability.

Design and deliver production ready AI systems, ensuring reliability, scalability, observability, performance, and cost efficiency across the full lifecycle. 

Establish platform level AI and data components, including Model broker and multi-model routing strategies, orchestration and workflow services, evaluation and experimentation frameworks, prompt, configuration, and versioning systems and telemetry, monitoring, and cost governance tooling.

Lead the design of end-to-end AI data pipelines, including document ingestion, OCR, NER, and preprocessing, chunking, embeddings, vector stores, and hybrid search and offline and online evaluation for quality, latency, safety, and cost.

Responsibilities Continued:

• Partner with data and analytics teams to align AI solutions with measurable product and business outcomes. 
• Partner with Product Management to identify, shape, and validate AI driven opportunities that improve clinical, operational, and business outcomes. 
• Help product teams move from problem framing to AI enabled solutions, balancing feasibility, value, risk, and regulatory constraints. 
• Contribute to innovation efforts by evaluating emerging AI tools, models, and frameworks 
• Prototyping new approaches in collaboration with Advanced Technology 
• Translating successful experiments into scalable platform capabilities 
• Ensure all AI solutions are production grade, with strong practices for observability, logging, alerting, and incident response, performance testing and cost optimization, safe rollout, experimentation, and rollback strategies 
• Embed responsible AI and security by design principles, including RBAC, audit logging, and privacy controls, alignment with healthcare regulations and standards (e.g., HIPAA, NIST AI RMF, ONC aligned guidance) and close collaboration with Security, Privacy, Regulatory, and Platform teams 
• Mentor and coach senior and intermediate engineers, raising overall capability in AI and data architecture, production AI engineering practices, evaluation, experimentation, and systems thinking 
• Drive adoption of shared AI and data best practices, enabling teams to reuse platform components, contribute improvements back to shared frameworks, consistently evolve domain specific AI capabilities 

Qualification and Skills:

• Proven experience leading and delivering production AI systems across multiple engineering teams. 
• Strong background in AI engineering with a data first mindset, including experience with largescale data pipelines and analytics driven evaluation. 
• Strong background in AI engineering with a data first mindset, including experience with largescale data pipelines and analytics driven evaluation. 
• Hands on experience extending or building with modern AI/agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, LlamaIndex, or equivalent. 
• Deep understanding of RAG and agentic architectures, including embeddings, vector databases, evaluation harnesses, and cost/latency tradeoffs. 
• Experience designing or contributing to offline and online evaluation methodologies tied to product metrics and business outcomes
• Experience implementing security by design and responsible AI practices in regulated or enterprise SaaS environments

Preferred skills:

• Experience deploying AI systems in healthcare or other regulated industries (e.g., HIPAA aligned environments). 
• Familiarity with model serving infrastructure, model broker patterns, and multi-model routing for performance and cost optimization. 
• Experience partnering closely with Product Management to identify and deliver AI enabled product capabilities. 
• Master’s degree in Artificial Intelligence, Computer Science, or a related field, or equivalent practical experience.