Senior Manager – Engineering (Development, QA & AI Enablement)

Role Summary:

The Senior Manager – Engineering owns end-to-end engineering outcomes, with a core mandate to drive AI adoption across development and quality engineering. This role is accountable not only for delivery and quality, but also for measurable improvements in productivity, speed, and predictability through AI-enabled practices.

Success in this role requires translating AI capabilities into practical, scalable engineering workflows—not experiments or isolated pilots—that are embedded into daily execution.

Location: Calicut

Reporting To: COO

Job Level: M3

Team Scope

• Direct reports: Engineering Managers / QA Managers / Tech Leads (6–8)
• Total organizational ownership: Development and QA teams (50–60 engineers)

Key Responsibilities:

1. Engineering Strategy & AI-Led Transformation

• Define and execute an engineering strategy that embeds AI into daily development and QA workflows.
• Identify and drive high-impact 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 improves delivery speed, quality, and predictability.

2. Delivery, Execution & Productivity Outcomes

• Own delivery commitments across multiple development and QA teams.
• Leverage AI-driven tools 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, including:
• Reduction in manual effort
• Improvement in automation coverage
• Cycle time and throughput gains
• Hold managers accountable for adoption and outcomes , not awareness.

3. AI-Enabled Quality Engineering

• Drive a shift from manual-heavy QA to AI-augmented quality engineering.
• 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 using 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
• Long-term maintainability
• Partner with Security and IT to ensure compliant use of AI tools.

5. People Leadership & Capability Building

• Hire and develop engineering leaders who champion AI-enabled ways of working.
• Upskill managers and senior engineers to:
• Identify meaningful AI use cases
• Coach teams on practical adoption
• Measure real, sustained outcomes
• Set clear 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 QA 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 Development and QA 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 and QA workflows
• Teams deliver faster without compromising quality
• Manual QA effort reduces materially quarter-over-quarter
• Managers independently drive AI adoption within their organizations
• Leadership sees clear ROI from AI initiatives—not hype