Senior Manager – Engineering & ACES

Role Summary:

The Senior Manager – Engineering and ACES (Product Management and Customer Excellence) is responsible for providing strategic and operational leadership across both the Engineering and ACES functions. The role oversees end-to-end engineering delivery and product group management, ensuring seamless collaboration between Engineering and ACES to deliver high-quality, customer-centric solutions aligned with business objectives.

This role is accountable not only for delivery, quality, and customer outcomes, but also for driving measurable improvements in productivity, speed, and operational excellence through AI-enabled practices across both Engineering and ACES. Success in this role requires translating AI capabilities into practical, scalable workflows that deliver tangible business value, moving beyond isolated experiments or proof-of-concepts to organization-wide adoption.

Location: Calicut

Reporting To: COO

Job Level: M3

Team Scope

• Direct reports: Engineering Managers / Engineering Leads / Tech Leads (6–8)/ACES leads
• Total org ownership: Engineering teams (50–60 engineers)

Key Responsibilities:

1. Engineering Strategy & AI-Led Transformation

• Define and execute an engineering strategy that embeds AI into daily engineering workflows.
• Identify high-impact opportunities for AI adoption across:
• Code generation and refactoring
• Test case creation and maintenance
• Test automation acceleration
• Defect analysis and root-cause identification
• Release validation and regression reduction
• Ensure AI adoption directly supports delivery speed, quality, and predictability.

2. Delivery, Execution & Productivity Outcomes

• Own delivery commitments across multiple engineering teams.
• Use AI-driven tooling and practices to:

Reduce cycle time and rework

Improve sprint predictability

Increase engineer productivity without increasing burnout

• Establish and track engineering and AI adoption metrics, such as:

Reduction in manual effort

Automation coverage improvement

Cycle time and throughput gains

• Hold managers accountable for adoption, not awareness.

3. AI-Enabled Quality Engineering

• Drive a shift from manual-heavy engineering practices to AI-augmented engineering workflows.
• Ensure quality is built in through:

AI-assisted test generation

Smarter regression selection

Early defect detection (shift-left)

• Reduce production defects and post-release escalations through AI-driven insights.
• Ensure AI tools are used responsibly, securely, and consistently across teams.

4. Technical Leadership & Governance

• Set clear standards for responsible and effective use of AI in engineering.
• Review and guide architectural decisions involving AI-enabled systems and tools.
• Balance speed of adoption with:

Code quality

Security and IP protection

Maintainability

• Partner with Security and IT teams to ensure compliant use of AI tools.

5. People Leadership & Capability Building

• Hire and develop engineering and ACES leaders who champion AI-enabled ways of working.
• Upskill managers and senior engineers to:

Identify AI use cases

Coach teams on practical adoption

Measure real outcomes

• Set expectations that AI adoption is part of performance, not optional learning.
• Build a culture of experimentation with accountability for results.

6. Cross-Functional & Executive Alignment

• Partner with Product, IT, Security, and Data teams to align AI initiatives.
• Communicate progress, risks, and ROI of AI adoption clearly to senior leadership.
• Convert AI initiatives into clear business narratives, not technical demos.
• Proactively surface areas where AI is underutilized and address root causes.

Success Metrics

This role is explicitly measured on AI-driven impact, including:

• Delivery predictability and on-time releases
• Measurable productivity gains from AI adoption
• Reduction in manual Engineering effort and regression cycles
• Improvement in defect leakage and production incidents
• Consistent AI adoption across teams (not isolated pockets)
• Engineering leadership readiness for future scale

Required Qualifications:

Experience

• 12–16+ years in software engineering roles
• 5+ years leading multiple engineering teams or managers
• Proven ownership of both engineering organizations
• Demonstrated experience driving process or technology transformation at scale

Technical & Leadership Skills

• Strong understanding of:
• Modern software engineering practices
• Test automation and CI/CD pipelines
• Practical application of AI tools in engineering workflows
• Ability to translate emerging technologies into repeatable execution models.
• Strong judgment, prioritization, and communication skills.

Preferred Qualifications

• Experience leading AI- or automation-led transformation programs
• Exposure to platform or large-scale product engineering
• Experience working in security-, compliance-, or regulation-aware environments
• Proven ability to build strong engineering leadership benches

What Success Looks Like (12–18 Months)

• AI is embedded into daily engineering workflows
• Teams deliver faster with no compromise on quality
• Manual engineering effort reduces materially quarter-over-quarter
• Managers independently drive AI adoption within their teams
• Leadership sees clear ROI from AI initiatives, not hype
• Engineering teams demonstrate measurable improvements in productivity, quality, and delivery predictability through AI-enabled practices