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Job Description
Role Overview
The AI Systems Engineer at Precision AI will focus on designing, building, and deploying agentic AI tools and systems, both for internal use and directly into the hands of farmers and agricultural operators. This role sits alongside our Infrastructure & DevOps Engineer on the core software team and will contribute to one of two critical tracks: our Velocity Engine release (our internal AI model) or the external deployment of AI models and tools to end users.
We're looking for someone who has lived and breathed agentic systems; or, if you're newer to agents, an exceptional software developer with a deep passion for AI and a drive to build production-grade systems in a fast-moving startup.
Working closely with AI/ML and Engineering teams, this role requires someone comfortable operating in ambiguous, fast-paced startup environments without relying on formal processes or extensive documentation.
This role is hybrid working out of our Southeast Calgary office 3 days per week.
Key Responsibilities
Agentic AI Development
- Design, build, and iterate on agentic AI tools and workflows using orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen); deploy production-grade systems used internally and by external users.
- Contribute to the development and external release of AI models and tools into the hands of farmers and agricultural operators.
- Support or lead work on the Velocity Engine depending on team priorities and business needs at time of hire.
Production Deployment & Operations
- Own end-to-end deployment of AI systems from development through staging to production; ensure systems are reliable, observable, and operationally sound.
- Monitor AI system performance, respond to incidents, and implement improvements in a high-velocity startup environment.
- Collaborate with the Infrastructure & DevOps Engineer to ensure AI workloads are supported by scalable, secure cloud infrastructure.
Quality & Collaboration
- Implement evaluation frameworks, testing, and monitoring for AI systems in production; track model performance, cost, and reliability over time.
- Collaborate with AI/ML and product teams on requirements, integration needs, and tooling to accelerate delivery.
Relevant Experience (Ideal Candidate Background)
- Hands-on experience building and deploying agentic AI systems (LLM-based agents, tool use, multi-step reasoning pipelines, RAG, orchestration frameworks) in production environments — or — strong software engineering fundamentals with demonstrated passion for AI and agents.
- Proficiency in Python and modern AI/ML libraries; experience designing and consuming APIs in production contexts.
- Familiarity with model deployment, prompt engineering, evaluation, and monitoring in real-world applications.
- Experience working with cloud platforms (AWS preferred) and containerized (Docker) application deployment.
- Comfortable operating in ambiguous, resource-constrained startup environments with limited documentation and process.
What You Bring
- Genuine obsession with AI and agents; you follow the frontier and know what's worth building with.
- Bias for shipping — you build production systems, not demos or prototypes, and take ownership through deployment and beyond.
- Ability to operate across internal tooling and external product work; comfortable switching contexts without losing quality.
- Collaborative yet autonomous working style; excited by breadth and impact over narrow specialization.
Bonus
- Startup background with end-to-end ownership of AI product or tooling development.
- Exposure to agricultural technology, robotics, autonomous systems, or developer platforms.
- Familiarity with AWS infrastructure (Lambda, ECS, S3, CloudWatch, etc.) in production AI contexts.
- Open-source contributions that demonstrate ownership — published, maintained, or supported a project.