Development Manager

Belonging to Air Canada is about belonging to a Canadian symbol, Air Canada recently voted best airline in North America. Take your career to new heights by joining our innovative and diverse team at the forefront of passenger air transport.

Join Air Canada — an iconic Canadian brand and the top-ranked airline in North America — as we continue to evolve our digital platforms to support critical operational and customer experiences.

The Development Manager is a seasoned and highly hands-on developer who drives sprint execution and delivery outcomes within a dedicated agile delivery team, adopting an AI-driven agentic engineering approach as the default way of working. The incumbent will take ownership of delivery predictability, code quality, and release readiness while executing solutions aligned with the technical direction, architecture, and standards established by the Tech Lead and Head of Development.

As the most senior developer or manager within the dedicated team, the Development Manager provides day-to-day technical leadership by coordinating delivery, coaching, and making hands-on contributions. This person sets up and executes the dedicated team's AI agent workflows (coding, testing, reviewing, and documentation agents operating under human oversight), achieves measurable productivity gains, and encourages every developer on the dedicated team to work the same way. The Development Manager is the primary implementation authority within approved architectural and responsible AI constraints, escalating design decisions that fall outside defined patterns and ensuring that operational lessons learned are integrated into the task backlog.

Responsibilities:

• Lead sprint commitments, ensure delivery predictability, and guarantee expected outcomes for the dedicated team; proactively manage scope, dependencies, and trade-offs, and ensure stories are well-defined, estimated, sequenced, and delivered as agreed.
• Break down complex requirements into clear technical tasks and agent-ready specifications (acceptance criteria, constraints, test cases) that AI engineers and coding agents can execute; anticipate risks early and guide the team through implementation and validation.
• Act as the primary implementation authority within approved architectural constraints; apply and enforce established patterns and standards, and escalate design decisions that exceed fixed constraints to the Tech Lead.
• Lead by example in AI-driven agentic development: use AI coding agents (e.g., GitHub Copilot, Cursor, Claude Code) daily for concept exploration, implementation, test generation, refactoring, and documentation, and coach the dedicated team to adopt the same practices.
• Design, configure, and maintain the dedicated team's agentic development workflows, including multi-agent configurations with clearly defined roles (e.g., implementation lead, reviewer, test writer), version control agent instructions, repository coding standards, scoped tool permissions, and reusable prompts and runbooks.
• Securely connect AI agents to the dedicated team's real working systems (repositories, CI/CD pipelines, issue tracking, cloud services, observability) through governed integrations such as Model Context Protocol (MCP) servers, applying the principle of least privilege and proper secret management in partnership with Cybersecurity.
• Establish human-in-the-loop constraints: no destructive, production-impacting, or agent-visible actions without human approval, and no AI-generated changes without independent review and verification through testing.
• Foster high-quality peer code reviews and merge request reviews, including for AI-generated code, focusing on correctness, maintainability, performance, reliability, security, and adherence to approved patterns.
• Take ownership of the dedicated team's testing strategy, including test-driven development, unit, integration, and API test automation, and AI coverage evaluation to ensure continuous compliance with quality criteria and prevent defects.
• Ensure production deployment readiness, including compliance with quality criteria (tests, security checks, performance), establishment of runbooks and operational documentation, observability coverage, and rollback and mitigation plans.
• Respect and enforce technical standards for coding, testing, documentation, security, observability, and responsible AI practices within the dedicated team.
• Increase CI/CD pipeline efficiency and automate AI-assisted development processes (e.g., automated AI review and automated test generation in pipelines) to improve deployment frequency and developer productivity.
• Lead and support the implementation of complex or high-risk features, striking a balance between delivery timelines, technical quality, and operational risk.
• Collaborate with Product Owners, QA leads, designers, and Scrum facilitators to ensure clarity, feasibility, testability, and timely decision-making regarding project scope changes.
• Take first-line responsibility for production issues and technical integrations related to the dedicated team; coordinate triage (using AI-assisted diagnostics where applicable), restore service, and communicate status and mitigation measures to stakeholders.
• Conduct root cause analyses for incidents and defects falling under the dedicated team's responsibility and implement corrective and preventive measures; ensure that lessons learned translate into product backlog items that are carried through to completion.
• Establish and maintain clear, accurate, and up-to-date documentation on technical specifications, prompts, and agent instructions to facilitate maintenance, onboarding, and operational support.
• Monitor and improve delivery, quality, and AI impact metrics, such as defect rates, pull request processing times, deployment frequency, rollback frequency, AI-assisted change sharing, and cycle time improvement; use data to report on measurable gains and drive improvement actions.
• Evaluate and prototype emerging AI development tools and techniques; share what works with the Tech Lead and contribute dedicated team learnings to TDS leadership bootcamps, runbooks, and communities of practice.
• Provide daily technical coaching and mentoring through pair working (particularly on agent workflows), feedback, and guidance; foster a culture centered on craftsmanship, accountability, collaboration, and continuous improvement.
• Design, develop, integrate, maintain, and troubleshoot software applications and services; write, review, test, and debug code for new features and enhancements, and analyze requirements, dependencies, and system interactions to recommend implementation approaches.

Personal Qualities

• Inspire and energize Engineering teams by demonstrating high enthusiasm and fostering a culture of excellence and empowerment.
• Demonstrate an innovative and forward-looking mindset, constantly exploring emerging technologies and non-traditional solutions to tackle complex challenges.
• Show curiosity and a hands-on spirit to build AI agents used as common technical tools, demonstrating rigor in verifying results before production deployment.
• Establish and maintain a culture of trust, transparency, and psychological safety to enable open dialogue that encourages diverse viewpoints.
• Challenge assumptions and the status quo with curiosity and courage to drive continuous improvement and increase team performance.
• Ensure a balance between strategic vision and practical technical guidance, ensuring alignment between business goals and technical execution.
• Lead by example through integrity, resilience, and a commitment to mentoring colleagues to reach their full potential.

Qualifications

Mandatory

• Bachelor's degree in computer science or a related field.
• At least five (5) years of software development experience in a production environment.
• Proven experience leading project implementation within agile teams, with accountability for sprint commitments, implementation results, and code quality.
• Strong hands-on expertise in modern programming languages and frameworks (e.g., Java, Node.js, TypeScript, Python, React, React Native, Angular, NestJS) and a solid foundation in APIs, event-driven architecture, and cloud computing.
• Proven experience in the daily, hands-on use of AI-assisted and agentic development tools (e.g., GitHub Copilot, Cursor, and Claude Code) in production work, with concrete examples of measurable impact on delivery speed or quality.
• Demonstrated ability to write prompts and agent instructions, as well as critically review and validate AI-generated code (spotting hallucinations, insecure patterns, and missing tests).
• Strong experience using continuous integration and deployment pipelines, test-driven development, automated testing, and modern devops practices.
• Proven ability to enforce engineering standards, ensure code quality, and support sprint-level delivery commitments.
• Excellent communication skills and the ability to collaborate effectively with both technical and non-technical colleagues.
• Punctuality and proven reliability to drive overall team success in a fast-paced environment.

Assets

• Experience designing or orchestrating multi-agent development workflows (agent roles, scoped tool permissions, persistent memory/context, automated review checks).
• Experience connecting AI agents to engineering systems using Model Context Protocol (MCP) or similar tool integrations.
• Experience integrating AI capabilities into products (Large Language Model APIs, Retrieval-Augmented Generation, tool calling), as well as applying AI evaluation frameworks and responsible AI practices.
• Experience with cloud platforms (AWS, Azure, GCP) and cloud architecture patterns; cloud service certifications (e.g., AWS Solutions Architect or Developer) an asset.
• Knowledge of API platforms, integration technologies, or enterprise systems.
• Experience gained in large-scale, regulated, or mission-critical environments such as the airline industry.
• Portfolio (e.g., GitHub, side projects already launched) demonstrating software developed from start to finish using agentic workflows.