Job Description

Job Summary

Job Description Capability Head – AI Engineering (Growth Markets 1) Minimum Eligibility: 10+ years of experience Technical Certifications in any one of AI Vendors Band: E6. Role Purpose The Capability Head – AI Engineering is responsible for establishing, leading, and scaling the AI Engineering capability across Growth Markets. The role drives AI solution industrialization, delivery excellence, capability development, sales enablement, governance, and innovation to accelerate AI adoption across customer engagements. The person will work closely with business leaders, account teams, delivery organizations, cloud ecosystems, and the central CTO AI organization to develop scalable, responsible, and commercially viable AI solutions that create measurable business outcomes. This role combines strategy, architecture leadership, capability building, innovation, sales support, governance, and operational excellence. Key Accountabilities 1. Capability Strategy & Leadership • Define and execute the AI Engineering capability strategy for Growth Markets. • Establish the vision, objectives, operating model, and roadmap for the capability. • Align regional AI priorities with business growth objectives and CTO AI strategy. • Build a sustainable AI talent ecosystem and capability maturity model. • Drive AI adoption at scale across customer accounts and internal initiatives. 2. AI Engineering & Industrialization • Establish reference architectures, design principles, standards, and reusable frameworks for AI, GenAI, ML, and Agentic AI solutions. • Drive industrialization of AI assets through reusable accelerators, templates, patterns, and MLOps practices. • Promote standardization, scalability, governance, and reusability across engagements. • Ensure delivery teams leverage established AI engineering practices. 3. Agentic AI & Intelligent Automation • Lead creation of intelligent agents and AI-enabled automation solutions. • Drive adoption of Agentic AI frameworks and productivity solutions. • Establish best practices for AI-enabled business workflows and autonomous systems. • Identify opportunities to improve operational efficiency through AI. 4. Sales & Go-To-Market Enablement • Support strategic pursuits, proposals, and RFP responses involving AI capabilities. • Create AI value propositions, solution offerings, case studies, and accelerators. • Partner with sales organizations to build pipeline and market differentiation. • Conduct executive-level client discussions, workshops, demonstrations, and innovation sessions. 5. Delivery Enablement & Governance • Provide technical governance and architecture oversight for AI engagements. • Conduct solution reviews and ensure adherence to engineering standards. • Support account teams in AI opportunity qualification and solution assurance. • Drive adoption of AI delivery playbooks, accelerators, and reusable assets. 6. Capability Development & Talent Management • Build and lead a high-performing AI Engineering team. • Establish competency frameworks, learning pathways, mentoring programs, and career tracks. • Drive workforce planning aligned to market demand and capability growth. • Promote certification, hands-on learning, hackathons, and community initiatives. 7. Responsible AI & Compliance • Ensure ethical, explainable, secure, and compliant use of AI technologies. • Implement governance frameworks defined by the CTO and global AI councils. • Promote responsible AI practices across all engagements. • Monitor compliance with AI governance standards and regulatory requirements. 8. Innovation & Ecosystem Collaboration • Partner with the CTO AI organization to adopt emerging technologies and shared intellectual property. • Evaluate new AI offerings and market opportunities. • Drive innovation initiatives, proof-of-concepts, and customer pilots. • Collaborate with hype

Key Responsibilities

Key Responsibilities

Business Leadership

• Own capability growth, adoption, and market relevance.
• Support revenue growth through AI-led opportunities.
• Build AI-led transformation offerings for customers.
• Serve as executive sponsor for key AI initiatives.

Technology Leadership

• Define AI engineering standards and architecture principles.
• Govern AI platform adoption and engineering practices.
• Drive MLOps, GenAI, Agentic AI, and solution industrialization.

People Leadership

• Lead architects, AI engineers, ML engineers, automation specialists, and capability leaders.
• Build succession plans and leadership pipelines.
• Develop communities of practice and knowledge-sharing forums.

Operational Leadership

• Establish governance mechanisms, review boards, and KPI dashboards.
• Track capability performance and maturity.
• Drive continuous improvement and operational excellence.

Skill Requirements

Required Skills & Experience

Technical Expertise

Strong understanding of:

• AI/ML Engineering
• Generative AI
• Agentic AI
• Retrieval Augmented Generation (RAG)
• LLM Fine-Tuning
• MLOps Platforms
• Data Engineering Foundations
• Cloud AI Services (Azure, AWS, GCP)
• DevOps, CI/CD, Containerization, and Platform Engineering
• Responsible AI and AI Governance

Leadership Experience

• Proven experience leading architecture, AI engineering, platform engineering, or technology

capability organizations.

• Experience building and scaling multidisciplinary teams.
• Demonstrated ability to influence senior business and technology stakeholders.
• Experience supporting strategic pursuits and large transformation programs.

Consulting & Communication

• Executive stakeholder management.
• Solution storytelling and value articulation.
• Business case development.
• Customer-facing consulting and advisory skills.

Other Requirements