The AI Lead is responsible for end‑to‑end AI solution architecture and technical leadership across complex client engagements. This role drives scalable, secure, and enterprise‑grade AI implementations, mentors delivery teams, and ensures high‑quality outcomes aligned with GT standards, Responsible AI principles, and business objectives.
Key Responsibilities
- Own AI solution architecture, technical design, and implementation strategy
- Lead AI delivery across multiple workstreams and teams
- Define and enforce standards for AI, GenAI, data, and integration patterns
- Design scalable RAG, agent‑based, and orchestration architectures
- Guide teams on AI governance, security, privacy, and Responsible AI
- Lead client architecture discussions, technical workshops, and executive demos
- Review solution designs, code patterns, and delivery artifacts
- Mentor and coach consultants, senior consultants, and associates
Core Tools & Technologies
Microsoft Ecosystem
- Azure AI Foundry / Azure AI Studio
- Azure OpenAI
- Advanced prompt engineering
- RAG, agent orchestration, evaluation frameworks
- Copilot Studio (Premium)
- Enterprise copilots, plugins, Direct Line, voice capabilities
- Azure AI Search
- Azure Integration Services
- Azure Functions, Logic Apps
- Service Bus, Event Grid
- Power Platform
- Enterprise‑grade Power Apps, Dataverse, advanced automation
- CI/CD pipelines using Azure DevOps
AWS & Google Cloud
AWS AI / GenAI
Amazon Bedrock (LLM orchestration, multi‑model strategies)
AWS Lambda, event‑driven architectures
S3 and data integration patterns
Google Cloud AI
Vertex AI (GenAI workflows, model integration)
BigQuery (analytics and AI‑driven insights)
GenAI / LLM Platforms
Anthropic Claude
Advanced reasoning, summarization, and enterprise GenAI use cases
Experience designing multi‑LLM and vendor‑agnostic AI architectures
Programming & Engineering
Python (solution frameworks, orchestration, tooling)
REST APIs, integration design
Secure enterprise integration patterns
Skills Required
- Strong architecture and solution design skills
- Proven technical leadership and mentoring capability
- Deep understanding of enterprise AI and GenAI patterns
- Stakeholder management, risk identification, and mitigation
- Ability to translate business strategy into scalable AI platforms
Required Certifications:
Microsoft Certified: Azure AI Engineer Associate (AI‑102)
Or
AWS Certified Machine Learning – Specialty
Or
Google Professional Machine Learning Engineer
