aecc - digital innovation hub
Website:
aeccglobal.io
Job details:
Role Purpose
The 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 stable
Core Responsibilities
1. 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 development
2. 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 validation
3. 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 sequencing
4. 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 delivery
5. 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 management
What 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-offs
Required 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
Click on Apply to know more.