AI Software Engineer

Compensation: $130,000 - $155,000 Job Title:

AI Software Engineer Location:

Cambridge, ON | On-site Job Type:

Full-Time Compensation: $130,000 - $155,000 Benefits:

RRSP, health/dental/vision package, reimbursement for tuition and professional dues, paid vacation, personal days, and sick days Get To Know Us

Eclipse Automation delivers cutting-edge custom automated manufacturing solutions across multiple industries. We combine advanced automation expertise with digital innovation to create smart, efficient, and sustainable manufacturing systems. Our global network includes facilities in Canada, the United States and Hungary ensuring regional expertise and global strength. The Position

Eclipse Automation is advancing a multi-year digital transformation that connects AI, engineering data, simulation, and enterprise systems to change how custom automation machines are designed, built, delivered, and supported. We are building AI services and agents that give employees better access to institutional knowledge, automate complex workflows, and connect information across engineering and business systems. We need a AI Software Engineer who can turn architecture and technical plans into secure, reliable, production-ready solutions. Approximately 70–80% of the role will focus on AI systems, agents, retrieval, evaluation, and model integration. The remaining work will apply full-stack development, APIs, data pipelines, and enterprise integration to support those solutions. What You’ll Be Doing

Build AI Systems and Agents Implement AI assistants, agents, retrieval-augmented generation systems, intelligent search, knowledge workflows, automation services, and reusable AI platform components. Translate approved architecture, requirements, constraints, and acceptance criteria into working software, testable increments, deployment plans, and maintainable technical documentation. Build tool-using and multi-step agent workflows that safely interact with enterprise APIs, databases, files, engineering systems, and approved business processes. Implement model orchestration, prompt and context management, structured outputs, memory, retrieval, embeddings, reranking, guardrails, human review, and failure-handling patterns. Create automated and human evaluation workflows for quality, groundedness, task completion, safety, latency, reliability, regression detection, and business usefulness. Prototype alternatives quickly, measure results, document findings, and help the Principal AI Architect make evidence-based architecture and technology decisions. Support both locally hosted and approved cloud AI models and services while respecting security, privacy, performance, capacity, and cost requirements. Develop Full-Stack Applications and Services

Build web applications and portals used by engineers, project teams, and leadership, with a focus on interfaces that support AI-enabled workflows. Develop responsive React and TypeScript frontends that make complex AI and engineering capabilities understandable and usable. Build backend APIs, services, workers, and integration components using Python and one or more of .NET/C# or Node.js. Create secure authentication, authorization, validation, error handling, logging, configuration, and administrative capabilities required for production operation. Design maintainable service boundaries and interfaces that can be reused across multiple AI use cases instead of creating isolated one-off applications. Connect Enterprise Data and Engineering Systems

Build and consume REST, SOAP, GraphQL, event, and file-based interfaces that connect AI solutions to internal systems and approved external services. Develop data pipelines and services that move, transform, contextualize, index, and synchronize information across CAD/PDM, PLM, ERP, MES, databases, file stores, and OpenUSD/Omniverse layers where required. Work with SQL databases, including MySQL and PostgreSQL, and with vector-search or hybrid-retrieval technologies used by AI applications. Protect production data and system stability while modernizing around long-lived applications, schemas, interfaces, and operational constraints. Diagnose data-quality, identity, permissions, performance, and integration issues across system boundaries and communicate risks early. Test, Deploy and Operate

Write clean, testable, well-documented code with appropriate unit, integration, end-to-end, load, security, and AI-evaluation coverage. Use Git-based workflows, pull requests, automated quality checks, and CI/CD through Azure DevOps, GitHub Actions, or similar platforms. Containerize and deploy services to Azure OpenShift or other approved environments, monitor production behaviour, troubleshoot failures, and improve reliability based on real usage. Instrument applications and AI workflows with useful logs, metrics, traces, evaluation results, usage data, and alerts while prote