Job Summary
We are seeking a hands-on Lead Developer to design, develop, test, and deploy an AI-driven asset management and maintenance application. The project involves enhancing the existing ADAM2 asset management solution with AI agents, workflow automation, and enterprise system integrations to improve maintenance efficiency, reduce costs, and support operational decision-making. The role requires strong production development experience with TypeScript, Node.js, Python, and AWS, along with expertise in agentic AI, LLM orchestration, and enterprise integrations. The successful candidate will provide technical leadership, make architectural decisions, guide a small development team, and remain actively involved in writing production code while delivering functional software through iterative development and stakeholder demonstrations.
Key Responsibilities
• Design, develop, test, and deploy application components using TypeScript, Node.js, Python, and AWS.
• Build AI agents and multi-step workflows involving tool calling, conditional routing, retrieval, and structured outputs.
• Integrate the application with Corrigo and other enterprise systems, APIs, databases, and data sources.
• Determine when to use deterministic business rules, conventional algorithms, or LLM-based reasoning to address business requirements.
• Implement prompt engineering, guardrails, output validation, and AI evaluation and testing mechanisms.
• Develop and maintain AWS infrastructure using Terraform and support CI/CD, logging, tracing, monitoring, and troubleshooting.
• Conduct technical feasibility assessments covering data availability, integration constraints, and implementation approaches.
• Lead technical design and implementation, review code, and guide other developers while remaining hands-on with production development.
• Deliver working functionality iteratively, conduct stakeholder demonstrations, and incorporate feedback into subsequent development cycles.
• Perform unit, integration, regression, and technical quality assurance testing, including AWS log and trace reviews where applicable.
• Enhance work order bundling by identifying additional maintenance activities that can be completed during existing site visits to reduce travel, mobilization costs, and operational disruption.
• Develop warranty intelligence capabilities to analyze asset information, warranty periods, construction and renovation records, and maintenance history to identify potentially covered repairs.
• Implement AI-based work order description quality checks to identify missing information, improve clarity and completeness, and support dispatch accuracy and first-time fix rates.
• Support conversational AI capabilities, where required, to enable users to query enterprise asset, work order, warranty, and maintenance data.
• Meet agreed Definition of Done criteria, produce technical documentation, and support knowledge transfer to the Suncor support team.
Required Qualifications
• Strong, recent hands-on experience with TypeScript and Node.js, including production applications, backend services, APIs, and asynchronous workflows.
• Significant hands-on experience developing, deploying, securing, and troubleshooting cloud-native applications on AWS.
• Experience with relevant AWS services, including Lambda, API Gateway, S3, IAM, CloudWatch, ECS/Fargate, and SQS.
• Commercial experience building production-ready AI agents and LLM-powered applications involving multi-step workflows, tool calling, external integrations, and structured recommendations beyond basic chatbots and simple LLM API calls.
• Practical experience with LangChain, LangGraph, or equivalent frameworks for AI orchestration, including conditional routing, workflow state, tool integration, error handling, and structured output processing.
• Strong working knowledge of Python for AI libraries, application services, automation, data processing, or system integration.
• Experience integrating enterprise APIs, databases, and operational systems, including authentication, data transformation, reliability, and error handling.
• Hands-on experience with retrieval-augmented generation (RAG), prompt engineering, output validation, AI safety and quality controls, and evaluation and testing of LLM-generated results.
• Practical experience with Terraform, infrastructure as code, CI/CD, deployment automation, application monitoring, and troubleshooting.
• Experience making architectural decisions, leading small development teams, conducting code reviews, and guiding delivery while personally writing production code.
• Strong communication and consulting skills, with experience working directly with stakeholders, assessing technical feasibility, managing evolving requirements, and delivering software iteratively.
Preferred Qualifications
• Direct experience with Amazon Bedrock and AWS-native generative AI services.
• Experience with Bedrock AgentCore or equivalent managed agent runtimes.
• Familiarity with AWS Kiro or similar AI-assisted development tools.
• Experience with Corrigo or comparable asset and work order management systems.
• Experience with maintenance scheduling, optimization, warranty management, or field service applications.
• Experience in Oil & Gas, utilities, facilities management, or industrial asset management.
