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.

- 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.

- 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.

- 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 explicit ROI from AI initiatives, not hype

Engineering teams demonstrate measurable improvements in productivity, quality, and delivery predictability through AI-enabled practices

📌 Senior Manager – Engineering & ACES (Kozhikode)
🏢 Nucore Software Solutions
📍 Kozhikode