Job Title

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: CalicutReporting To: COOJob Level: M3Team ScopeDirect 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 TransformationDefine 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 refactoringTest case creation and maintenanceTest automation accelerationDefect analysis and root-cause identificationRelease validation and regression reductionEnsure AI adoption directly improves delivery speed, quality, and predictability.2. Delivery, Execution & Productivity OutcomesOwn delivery commitments across multiple development and QA teams.Leverage AI-driven tools and practices to:Reduce cycle time and reworkImprove sprint predictabilityIncrease engineer productivity without increasing burnoutEstablish and track engineering and AI adoption metrics, including:Reduction in manual effortImprovement in automation coverageCycle time and throughput gainsHold managers accountable for adoption and outcomes, not awareness.3. AI-Enabled Quality EngineeringDrive a shift from manual-heavy QA to AI-augmented quality engineering.Ensure quality is built in through:AI-assisted test generationSmarter regression selectionEarly 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 & GovernanceSet 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 qualitySecurity and IP protectionLong-term maintainabilityPartner with Security and IT to ensure compliant use of AI tools.5. People Leadership & Capability BuildingHire and develop engineering leaders who champion AI-enabled ways of working.Upskill managers and senior engineers to:Identify meaningful AI use casesCoach teams on practical adoptionMeasure real, sustained outcomesSet 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 AlignmentPartner 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 MetricsThis role is explicitly measured on AI-driven impact, including:Delivery predictability and on-time releasesMeasurable productivity gains from AI adoptionReduction in manual QA effort and regression cyclesImprovement in defect leakage and production incidentsConsistent AI adoption across teams (not isolated pockets)Engineering leadership readiness for future scaleRequired Qualifications:Experience12–16+ years in software engineering roles5+ years leading multiple engineering teams or managersProven ownership of both Development and QA organizationsDemonstrated experience driving process or technology transformation at scaleTechnical & Leadership SkillsStrong understanding of:Modern software engineering practicesTest automation and CI/CD pipelinesPractical application of AI tools in engineering workflowsAbility to translate emerging technologies into repeatable execution models.Strong judgment, prioritization, and communication skills.Preferred QualificationsExperience leading AI- or automation-led transformation programsExposure to platform or large-scale product engineeringExperience working in security-, compliance-, or regulation-aware environmentsProven ability to build strong engineering leadership benchesWhat Success Looks Like (12–18 Months)AI is embedded into daily engineering and QA workflowsTeams deliver faster without compromising qualityManual QA effort reduces materially quarter-over-quarterManagers independently drive AI adoption within their organizationsLeadership sees clear ROI from AI initiatives—not hype