Software Engineer, AI Automation

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Engineer, AI Automation based in United States .** This is a hands-on AI engineering opportunity focused on using artificial intelligence to automate complex manual work across a fast-growing technology organization. You will design, build, evaluate, and deploy AI agents that become part of the daily workflows of non-technical teams. The role combines application engineering with close business partnership, requiring you to understand how teams operate and translate real-world processes into reliable agent-driven solutions. You will work with large language models, Model Context Protocol (MCP), agentic workflows, evaluation frameworks, and production systems. You will have meaningful ownership from prototype through production, including permissions, human-in-the-loop controls, auditability, monitoring, and rollback strategies. Working closely with data engineers and business stakeholders, you will help establish scalable patterns for AI automation while challenging existing assumptions and improving the overall approach. This individual contributor role offers significant autonomy, technical depth, and the opportunity to build practical AI systems that deliver measurable value across the organization. ### Accountabilities - Build and ship AI-powered applications that automate manual and repetitive business processes across multiple internal teams. - Translate business processes, judgment calls, edge cases, and operational requirements into reusable skills that AI agents can reliably invoke. - Design and implement agentic workflows that combine skills, tools, and systems of record into production-ready solutions. - Connect agents to internal systems through Model Context Protocol (MCP) servers and expand MCP capabilities when required systems or data sources are not yet supported. - Define success criteria and establish evaluation standards with business stakeholders before building or deploying AI solutions. - Create evaluation datasets, establish performance baselines, and use objective evaluation results to determine whether an agent is ready for production. - Take AI agents from proof of concept through production deployment, including permissions, human-in-the-loop checkpoints, audit trails, rollback mechanisms, and ongoing support. - Monitor and improve agent behavior based on real-world usage, identifying failure modes and implementing appropriate safeguards and improvements. - Create clear documentation and establish ownership with business teams so deployed agents can be operated effectively without continuous engineering support. - Partner closely with data engineers to define requirements for data access, data models, data quality, and the information agents need to read or write. - Work directly with non-technical teams to understand their workflows, identify automation opportunities, and convert operational knowledge into technical requirements. - Challenge existing assumptions and proposed approaches using your practical experience with AI application development and agent architectures. - Use modern agentic coding tools to accelerate development and navigate unfamiliar codebases efficiently. - Maintain production-quality engineering practices across version control, continuous integration, containers, deployment, and operational support. - Identify and resolve ambiguity, undocumented processes, fragmented systems, and unclear ownership to move automation initiatives forward. **Requirements** - You have at least 5 years of hands-on software development experience, with demonstrated practical experience building and shipping production software. - You have substantial hands-on experience building applications powered by Large Language Models (LLMs), including prompting, tool use, agentic loops, and multi-step workflows. - You have shipped at least one AI agent or LLM-powered application that non-engineering users depend on in their day-to-day work. - You understand the practical limitations and failure modes of LLM-based systems and know how to design safeguards and controls around them. - You have practical experience with agent tooling, including MCP, skill or tool authoring, and orchestrating agents against real systems of record. - You have experience building evaluation datasets and using evaluation results to make practical decisions about AI system quality and production readiness. - You are fluent in Python and comfortable working with SQL and production data models. - You have experience using agentic coding tools such as Cursor to work efficiently within unfamiliar codebases. - You understand core software delivery practices, including Git, continuous integration, containers, deployment, and production operations. - You can work directly with non-technical business stakeholders to understand processes, identify requirements, and translate operational needs into reliable technical solutions without relying on a Product Manager to mediate every interaction. - You are comfortable working in ambiguous environments where processes may be undocumented, internal tools may be imperfect, and ownership may not always be clearly defined. - You demonstrate strong problem-solving skills and the ability to identify a path forward when requirements, data, or systems are incomplete. - You are self-directed, pragmatic, and outcomes-focused, with the ability to take ownership from discovery through deployment and ongoing support. - Experience building or maintaining MCP servers is considered a strong advantage. - Previous experience in solutions engineering, professional services, internal tools, or startup founding environments is a plus. - Familiarity with systems such as Linear, Jira, Zendesk, Salesforce, or Snowflake is beneficial. - Experience in insurance, fintech, healthcare, or another regulated environment where technical errors can create compliance or business risks is a plus. **Benefits** - Base salary of $128,500–$160,600 per year for candidates located in Alberta, British Columbia, or Ontario. - Base salary of $115,600–$144,540 per year for candidates located in other Canadian locations. - Compensation is determined based on factors including relevant education, skills, qualifications, experience, credentials, and geographic location. - 100% coverage for medical, dental, and vision benefits. - Flexible Paid Time Off (PTO). - Annual home office stipend and access to WeWork. - Mental and physical wellness programs, including resources such as Headspace and Lumino. - Opportunities for professional growth, advancement, and expanded technical ownership. - Remote-first working environment designed to support collaboration across distributed teams. - Opportunity to work on practical AI automation initiatives with organization-wide impact. - Exposure to emerging AI technologies, agent architectures, evaluation methodologies, and production AI engineering. - Opportunity to collaborate closely with engineering, data, and business teams while building solutions used by non-technical stakeholders. - A role with significant individual ownership and the opportunity to shape how AI automation is developed and deployed across the organization.