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Responsibilities
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Digital Engineering & AI Solution Delivery Leader is responsible for the end-to-end delivery of digital and AI-driven solutions—from project initiation through deployment and measurable client outcomes. This role combines strong engineering leadership with AI transformation expertise to design, build, deploy, and scale intelligent solutions. The leader will establish best-in-class AI and digital engineering practices, build high-performing teams, drive thought leadership, and ensure continuous improvement through feedback-driven iteration.
Key Responsibilities
- End-to-End Delivery Leadership
- Ensure institutionalization of best practices around effective design and delivery including delivery governance, risk management, and performance tracking across programs.
- Team Building & Capability Development
- Client Engagement & Value Realization
- Thought Leadership & Innovation
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Continuous Improvement & Iteration
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| Experiences |
- 20+ years in enterprise architecture, engineering, or solution leadership within consulting, SI, or digital practices.
- Experience partnering with senior client stakeholders including CIO, CTO, CDO, and business executives.
- Proven experience in architecting data-driven, AI-enabled enterprise solutions, as well as adoption of standardized AI engineering and delivery practices
- Strong understanding of cloud-native architectures, integration frameworks, data fabric/mesh, and AI orchestration.
- Strong stakeholder management and executive communication skills.
- Demonstrated experience leading large-scale digital transformation or AI transformation programs.
- Proven track record of delivering production-grade AI solutions at enterprise scale.
- Background in managing global delivery teams and distributed engineering organizations.
- Experience establishing AI engineering practices, reusable frameworks, and innovation labs.
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Skills
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- Deep understanding of AI/ML solution architecture, including machine learning, generative AI, NLP, computer vision, and predictive analytics.
- Experience with AI lifecycle management, including model development, evaluation, deployment, monitoring, and continuous improvement.
- Strong familiarity with MLOps / LLMOps practices, model governance, responsible AI, and ethical AI frameworks.
- Expertise in modern software engineering practices including microservices, API-driven architecture, and cloud-native development.
- Experience designing scalable digital platforms and distributed systems.
- Hands-on or strategic experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Strong program and portfolio delivery leadership, managing multiple concurrent transformation initiatives.
- Strong commercial acumen including solution shaping, proposal development, and value articulation.
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