Job Title

Role PurposeThe Engineering Manager is responsible for building a high-performing, predictable, and scalable engineering function. This role combines people leadership, hands-on technical contribution, delivery management, DevOps oversight, and architectural stewardship in close partnership with the Technical Architect.The Engineering Manager will lead engineering through a period of AI-enabled transformation, ensuring that modern AI-assisted development workflows increase speed and leverage without compromising quality, reliability, security, or architectural coherence. This includes treating AI adoption as a change-management and workflow-design challenge, not just a tooling upgrade.Key Outcomes (12–18 Month Horizon)• Engineering delivery is predictable, transparent, and well-paced• Engineering productivity increases through disciplined, effective use of AI-assisted tooling• AI-assisted development workflows are standardised, understood, and consistently applied• Product discovery and design consistently stay at least one sprint ahead of delivery• Tech leads operate with clear ownership and confident decision-making within agreed guardrails• Engineering, CRM, DevOps, and operational teams work as a cohesive system• Platform reliability, deployment confidence, and operational hygiene improve despite increased delivery velocity• AI experimentation accelerates learning while production quality and safety remain stableCore Responsibilities1. Engineering Leadership & Team Management• Lead and support multiple teams across engineering, QA, DevOps, CRM, and operations• Foster a culture of accountability, clarity, continuous improvement, and ownership of outcomes• Support engineers and leads as roles and practices evolve with AI-assisted development2. AI-Assisted Engineering, DevOps & Change Enablement• Lead AI adoption as a change-management initiative, addressing mindset, role evolution, and workflow redesign• Define and evolve a standard AI-assisted delivery workflow (e.g. brainstorm → spec → build → verify → review → release)• Establish guardrails to ensure AI increases speed without increasing risk, tech debt, or operational load• Enable Product, CRM, and operational teams to use AI to improve intake quality, specification clarity, and validation3. Delivery Management & Execution• Own delivery orchestration across all engineering and engineering-adjacent teams• Identify and resolve AI-amplified bottlenecks, including CI/CD speed, test reliability, validation, and review throughput• Act as the primary point of accountability for delivery commitments and sequencing4. Product Partnership & Discovery Enablement• Ensure discovery stays ahead of delivery through strong partnership with Product and Design• Define clear intake standards and explicit boundaries between experimentation and production delivery5. Technical Contribution, Architecture & DevOps• Provide valuable inputs during complex technical discussions and strategic decisions with engineering team• Remain hands-on where appropriate in complex or high-impact areas• Build and maintain context infrastructure that AI systems reliably consume, including standards, patterns, examples, and decision records• Promote best practices across CI/CD, cloud infrastructure, reliability, and incident managementWhat Success Looks Like• AI-assisted development is a trusted, normal part of daily work• Delivery velocity increases without increased defects or incidents• Product Managers focus on discovery rather than delivery coordination• Stakeholders experience fewer surprises and clearer trade-offsRequired Experience & Capabilities• 8+ years professional software engineering experience• 3+ years engineering leadership experience• Hands-on experience leading teams through AI-assisted development adoption• Well versed with public cloud i.e. AWS/GCP and in sync with latest trends in AI landscape• Strong judgement balancing speed, safety, and learning