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

Delivery, Execution & Productivity Outcomes\nOwn delivery commitments across multiple development and QA teams.\nLeverage AI-driven tools and practices to:\nReduce cycle time and rework\nImprove sprint predictability\nIncrease engineer productivity without increasing burnout\nEstablish and track engineering and AI adoption metrics, including:\nReduction in manual effort\nImprovement in automation coverage\nCycle time and throughput gains\nHold managers accountable for adoption and outcomes , not awareness.\n\n3. AI-Enabled Quality Engineering\nDrive a shift from manual-heavy QA to AI-augmented quality engineering.\nEnsure quality is built in through:\nAI-assisted test generation\nSmarter regression selection\nEarly defect detection (shift-left)\nReduce production defects and post-release escalations using AI-driven insights.\nEnsure AI tools are used responsibly, securely, and consistently across teams.\n\n4. Technical Leadership & Governance\nSet clear standards for responsible and effective use of AI in engineering.\nReview and guide architectural decisions involving AI-enabled systems and tools.\nBalance speed of adoption with:\nCode quality\nSecurity and IP protection\nLong-term maintainability\nPartner with Security and IT to ensure compliant use of AI tools.\n\n5.

People Leadership & Capability Building\nHire and develop engineering leaders who champion AI-enabled ways of working.\nUpskill managers and senior engineers to:\nIdentify meaningful AI use cases\nCoach teams on practical adoption\nMeasure real, sustained outcomes\nSet clear expectations that AI adoption is part of performance, not optional learning.\nBuild a culture of experimentation with accountability for results.\n\n6. Cross-Functional & Executive Alignment\nPartner with Product, IT, Security, and Data teams to align AI initiatives.\nCommunicate progress, risks, and ROI of AI adoption clearly to senior leadership.\nConvert AI initiatives into clear business narratives, not technical demos.\nProactively surface areas where AI is underutilized and address root causes.\n\nSuccess Metrics\nThis role is explicitly measured on AI-driven impact, including:\nDelivery predictability and on-time releases\nMeasurable productivity gains from AI adoption\nReduction in manual QA effort and regression cycles\nImprovement in defect leakage and production incidents\nConsistent AI adoption across teams (not isolated pockets)\nEngineering leadership readiness for future scale\n\nRequired Qualifications:\nExperience\n12–16+ years in software engineering roles\n5+ years leading multiple engineering teams or managers\nProven ownership of both Development and QA organizations\nDemonstrated experience driving process or technology transformation at scale\n\nTechnical & Leadership Skills\nStrong understanding of:\nModern software engineering practices\nTest automation and CI/CD pipelines\nPractical application of AI tools in engineering workflows\nAbility to translate emerging technologies into repeatable execution models.\nStrong judgment, prioritization, and communication skills.\n\nPreferred Qualifications\nExperience leading AI- or automation-led transformation programs\nExposure to platform or large-scale product engineering\nExperience working in security-, compliance-, or regulation-aware environments\nProven ability to build strong engineering leadership benches\n\nWhat Success Looks Like (12–18 Months)\nAI is embedded into daily engineering and QA workflows\nTeams deliver faster without compromising quality\nManual QA effort reduces materially quarter-over-quarter\nManagers independently drive AI adoption within their organizations\nLeadership sees clear ROI from AI initiatives—not hype