Practice Lead for AI

Job Summary

The Practice Lead – AI will lead the growth and development of the company’s Artificial Intelligence practice. This role is responsible for defining the AI strategy, developing AI solutions and service offerings, driving business development, and managing client engagements from initial discussions through delivery.

The ideal candidate combines strong AI and technology knowledge with business leadership, client management, and the ability to build high-performing teams. The role will focus on helping clients identify opportunities to use AI to improve business processes, increase efficiency, and support digital transformation.

Key Responsibilities

1. AI Practice Strategy and Leadership

• Define and execute the AI practice strategy aligned with business goals and market opportunities.
• Develop AI service offerings, solutions, and implementation frameworks.
• Identify emerging AI technologies and trends that can create business value for clients.
• Establish best practices for AI solution design, development, deployment, and support.
• Build and grow the AI practice in line with the company's business objectives.

2. Business Development and Client Engagement

• Identify new business opportunities and develop relationships with clients and technology partners.
• Lead client discussions, presentations, and solution proposals for AI-related projects.
• Work with sales teams to develop proposals, respond to RFPs, and support presales activities.
• Identify opportunities to expand existing accounts through AI solutions and services.
• Drive revenue growth, profitability, and achievement of practice targets.

3. AI Solution Development and Delivery

• Oversee the design and implementation of AI solutions based on client requirements.
• Guide teams in developing AI, Generative AI, machine learning, intelligent automation, and data-driven solutions.
• Ensure AI solutions are scalable, secure, reliable, and aligned with business needs.
• Oversee the delivery of AI projects and ensure quality, timelines, and client expectations are met.
• Promote the use of AI to improve business processes, customer experience, and operational efficiency.

4. Team Leadership and Capability Development

• Recruit, develop, and lead AI professionals, including AI engineers, data scientists, solution architects, and consultants.
• Build technical capabilities through training, mentoring, and knowledge sharing.
• Develop workforce plans based on project demand and business growth.
• Foster collaboration among AI, cloud, data, cybersecurity, and software engineering teams.

5. Governance and Responsible AI

• Establish guidelines for responsible AI development and deployment.
• Ensure AI solutions comply with relevant data privacy, security, regulatory, and governance requirements.
• Manage risks related to AI accuracy, bias, data quality, and model performance.
• Promote appropriate AI monitoring, evaluation, and continuous improvement.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
• At least 8–10 years of experience in IT, technology consulting, AI, data, or digital transformation, with relevant leadership experience.
• Proven experience leading AI initiatives, AI solution delivery, or technology practices.
• Experience in AI and Generative AI, machine learning, intelligent automation, or related technologies.
• Strong understanding of AI solution architecture, data platforms, cloud services, and enterprise integration.
• Experience in business development, presales, client engagement, and solution proposal development.
• Proven ability to manage teams, client engagements, budgets, and business targets.
• Strong communication, presentation, stakeholder management, and leadership skills.

Preferred Qualifications

• Experience in enterprise AI adoption and Generative AI implementation.
• Familiarity with AI platforms and services from Microsoft Azure, AWS, or Google Cloud.
• Knowledge of large language models (LLMs), retrieval-augmented generation (RAG), AI agents, and AI application development.
• Experience establishing AI governance, responsible AI practices, and enterprise AI standards.
• Relevant AI, cloud, or data certifications are an advantage.

Key Performance Indicators (KPIs)

• AI practice revenue growth and profitability.
• Number and value of new AI projects and client engagements.
• Success rate of AI proposals and presales opportunities.
• Quality, timeliness, and business impact of AI project delivery.
• Client satisfaction and account growth.
• Development and retention of AI talent.
• Adoption and commercialization of new AI service offerings.

Core Competencies

• AI Practice Leadership
• Business Development and Presales
• AI Strategy and Solution Architecture
• Client Relationship Management
• Project and Delivery Management
• People Leadership and Capability Building
• Commercial and Financial Management
• Responsible AI and Governance