Role Summary
The AI Practice Lead is a pivotal Player-Coach role, responsible for defining the strategic roadmap for our AI division while remaining deeply involved in technical architecture and solution design. You will bridge the gap between business vision and engineering execution, managing the P&L of the AI practice while leading client engagements to deliver high-impact, cloud-native AI solutions.
Experience: 1012 Years
Key Responsibilities
1. Strategic Leadership & Client Engagement
- Practice Growth: Define the AI service offerings, go-to-market strategy, and long-term roadmap for the AI practice.
- P&L Management: Oversee the budget, resource allocation, and profitability of AI projects.
- Advisory: Act as a trusted advisor to C-suite clients, translating complex AI concepts into clear business value and ROI.
2. Technical Architecture & Solution Design
- System Design: Architect scalable, production-grade AI/ML systems using .NET, Java, or Python frameworks.
- Cloud Orchestration: Design and deploy models using Azure ML, AWS SageMaker, or Google Vertex AI, ensuring cost-efficiency and performance.
- Hybrid Integration: Ensure seamless integration of AI components into existing enterprise application stacks.
3. Hands-on Execution & Governance
- Prototyping: Lead the development of PoCs (Proof of Concepts) and MVPs for high-stakes client bids.
- Quality Standards: Establish MLOps practices, coding standards, and ethical AI guidelines to ensure robust delivery.
- Mentorship: Lead a team of data scientists and engineers, providing technical oversight and career coaching.
Technical Skills & Qualifications
- Programming Mastery: Expert-level proficiency in Python, with a strong background in enterprise languages like .NET or Java.
- AI/ML Expertise: Proven experience with NLP, Computer Vision, Predictive Analytics, and Large Language Models (LLMs).
- Cloud Platforms: Hands-on experience with Azure Machine Learning, AWS SageMaker, or similar cloud AI ecosystems.
- Architecture: Strong understanding of microservices, API design, and data engineering pipelines.
- Business Acumen: Demonstrated experience in managing budgets, project margins, and business development.
Preferred Experience
- 10+ years in Information Technology, with at least 4 years specifically leading AI/ML initiatives.
- Experience in a client-facing consulting or professional services environment.
- Relevant certifications (e.g., Azure AI Engineer Associate, AWS Certified Machine Learning Specialty).
