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AI Director, Development Engineering

Greenway Health
Karnataka, IND
Onsite
Discovered 1 weeks ago
Engineering strategySDLC transformationAgile leadershipCloud application developmentCI/CDArchitecture governance
Free

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Key skills for this role

Engineering strategySDLC transformationAgile leadership
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Role Overview

Lead current and future development capabilities while shaping engineering strategy, delivery standards, architecture direction, and team performance.

Act as a change agent for AI, automation, DevSecOps, CI/CD, cloud practices, quality gates, and engineering intelligence.

Key Outcomes and Responsibilities

  • Define engineering strategy aligned with business and product goals.
  • Improve SDLC maturity, cloud delivery, architecture governance, and quality practices.
  • Lead product development teams across the software development lifecycle.
  • Establish standards for engineering, QA, DevOps, secure delivery, automation, and architecture governance.
  • Oversee analysis, design, programming, debugging, interoperability, and application modification.
  • Guide roadmap, resourcing, timing, technical debt, and business outcome trade-offs.
  • Lead, mentor, and develop high-performing individuals and teams.

Education and Experience

  • Bachelor’s degree in Computer Science or a related field is required.
  • Master’s degree is preferred.
  • Ten or more years of software development experience, including complex whole-lifecycle software development management.
  • Five or more years leading staff and five or more years in software product development or management.
  • Experience leading technology direction is preferred.

Skills and Tools

  • Engineering strategy, SDLC transformation, Agile leadership, cloud application development, CI/CD, and architecture governance.
  • DevSecOps, technical debt management, stakeholder influence, change leadership, and team development.
  • Tools and technologies include Jira, VersionOne, Azure DevOps, TFS, CI/CD platforms, cloud platforms, infrastructure as code, test automation, observability, and AI-enabled engineering tools.

AI Usage and Accountability

Use approved AI tools for strategy, architecture analysis, technical debt review, delivery-risk analysis, quality insights, documentation, communication, and portfolio trade-offs.

Validate AI-generated outputs before use and do not enter sensitive or restricted data into unapproved tools.

Human ownership remains required for strategy, architecture direction, people leadership, budget influence, risk acceptance, stakeholder alignment, and delivery outcomes.

Success Measures

Success is measured through engineering strategy execution, SDLC maturity, product delivery quality, cloud and CI/CD adoption, technical debt reduction, team performance, stakeholder trust, responsible AI adoption, and improved engineering outcomes.

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