Position : Head of Artificial Intelligence

Role Summary

The Head of Artificial Intelligence Practice (North America) leads strategy, growth, delivery excellence, and talent for the AI portfolio across U.S. and Canada. This executive role owns practice P&L, builds scalable offerings (GenAI, ML, data science, AI platforms, MLOps), and partners with sales and delivery leaders to expand revenue and customer impact across industries.

Reporting and Scope

• Reports to: Head of Data & AI - North America
• Direct reports: AI practice leadership team (solution leads, delivery leaders, architects), plus dotted-line matrix teams
• Geography: United States and Canada (remote/hybrid depending on location)
• Travel: Up to 25–40% (client sites, executive briefings, industry events)

Key Responsibilities

·       Practice strategy and roadmap

• • Define the North America AI practice strategy aligned to corporate goals and market demand.
  • Build and maintain a 12–24 month capability roadmap across GenAI, Agentic AI, applied ML, AI engineering, MLOps, and AI governance.
  • Identify strategic bets (industries, partnerships, platforms) and prioritize investments for impact and scale.
  • Lead the delivery team for AI across NA including building and managing talent and ensuring ulitization targets for the team

·       Portfolio, offerings, and thought leadership

• • Support Head of Advisory and Global Teams with packaged offerings and accelerators (POCs-to-production playbooks, reference architectures, reusable components).
  • Work with Head of Innovation industry-specific solutions (e.g., banking, retail, healthcare, telecom, public sector) with measurable outcomes.
  • Represent the company externally through speaking, publishing, analyst briefings, and customer success stories.

·       Go-to-market and revenue growth

• • Own pipeline and bookings targets for AI services in North America; partner with sales, alliances, and marketing.
  • Help Represent the solutioning for strategic pursuits, including executive-level proposal narratives, value cases, and pricing models.
  • Help support the partner ecosystem with hyperscalers and AI platform vendors; drive co-sell motions where applicable.

·       Delivery excellence and customer outcomes

• • Ensure high-quality delivery across AI engagements, with strong governance, risk management, and stakeholder communication.
  • Standardize delivery methodology for AI programs (discovery, prototyping, productionization, monitoring, continuous improvement).
  • Drive customer adoption, measurable business value, and referenceability through disciplined success management.

·       Talent, org design, and culture

• • Build and scale a high-performing AI team: hiring plans, skills frameworks, career paths, and mentorship.
  • Upskill broader delivery teams through training programs, communities of practice, and internal enablement.
  • Foster a culture of engineering rigor, responsible AI, collaboration, and continuous learning.

·       Governance, security, and responsible AI

• • Establish responsible AI standards (privacy, security, bias mitigation, explainability, model risk management).
  • Partner with security, legal, and compliance teams to ensure AI solutions meet client and regulatory requirements.
  • Define and monitor AI practice quality standards, including data governance, model lifecycle controls, and audits.

·       Financial management and operations

• • Help support the Head of Data and AI P&L: revenue, gross margin, utilization, bench, subcontractor mix, and investment planning.
  • Set operating rhythm: QBRs, forecast accuracy, capacity planning, and delivery health dashboards.
  • Optimize delivery model (onshore/nearshore/offshore) to meet client needs and margin targets.

Required Qualifications

• 15+ years of experience in technology consulting, product engineering, or enterprise technology leadership, with 8+ years in AI/ML/GenAI leadership.
• Proven track record building and scaling an AI practice or portfolio with P&L responsibility and measurable revenue growth.
• Strong understanding of modern AI stack: GenAI (LLMs), Agentic AI, ML, data engineering, AI platforms, MLOps/LLMOps, and cloud services (AWS/Azure/GCP).
• Demonstrated experience delivering AI solutions end-to-end: discovery to production, monitoring, and continuous improvement.
• Executive presence with ability to influence EVP-level stakeholders and lead complex deal pursuits.
• Experience leading multi-disciplinary teams (AI Architects, AI Engineers, data scientists, ML engineers, architects, product managers, delivery leaders).
• Knowledge of responsible AI, security, and privacy requirements for enterprise AI implementations.

Preferred Qualifications

• Experience in IT services/consulting organizations operating with global delivery models (onshore/nearshore/offshore).
• Domain expertise in one or more regulated industries (financial services, healthcare, telecom, government).
• Partnership experience with hyperscalers and AI platform vendors; co-sell and alliance management success.
• Hands-on experience with AI solution architecture, including retrieval-augmented generation (RAG), vector databases, and agentic workflows.
• Advanced degree in Computer Science, Engineering, Data Science, or related field; MBA a plus.

Core Competencies

• Strategic leadership and business building
• Consultative selling and executive stakeholder management
• AI engineering rigor and delivery governance
• Productization mindset (offerings, accelerators, repeatability)
• Talent development and organizational design
• Cross-functional collaboration in matrix environments
• Strong communication: narrative building, proposals, and presentations

Tools and Technologies (Representative)

• Cloud: AWS, Microsoft Azure, Google Cloud Platform
• AI/ML: Agentic AI, Python, PyTorch/TensorFlow, scikit-learn, MLflow (or equivalent), feature stores
• GenAI: LLM APIs and platforms, prompt engineering, RAG patterns, evaluation frameworks, guardrails
• Data: SQL, modern data warehouses/lakehouse platforms, streaming where needed
• MLOps/LLMOps: CI/CD for models, monitoring/observability, model registry, governance tooling

Work Environment

• Remote or hybrid within North America; may require proximity to major client hubs.
• Travel expectation up to 25–40% based on client needs and business development cycles.
• This role may require occasional work outside standard business hours to support executive meetings across time zones.