Role Overview:

We are seeking a highly experienced and strategic AVP - GenAI Architect to lead the architecture, design, and deployment of enterprise-grade Generative AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and emerging AI technologies.

The role will be responsible for defining and executing the organization's GenAI technology strategy, translating business opportunities into scalable AI solutions, and establishing architecture standards and engineering practices for production-grade GenAI implementations.

The ideal candidate will have deep expertise across LLMs, RAG architectures, AI agents, model selection, prompt engineering, vector databases, AI/ML pipelines, cloud platforms, MLOps/LLMOps, and enterprise integration. The candidate should be capable of designing solutions across AWS, Microsoft Azure, and Google Cloud Platform (GCP) and selecting the most appropriate technology based on business, technical, security, scalability, and cost considerations.

As a senior technology leader, the AVP will work closely with business leadership, product teams, data teams, engineering teams, cybersecurity, enterprise architecture, and external technology partners to drive GenAI adoption while ensuring responsible, secure, scalable, and measurable AI outcomes.

Key Responsibilities:

- Define and lead the architecture of enterprise-grade Generative AI solutions.

- Design scalable solutions leveraging:

1. Large Language Models (LLMs)

2. Retrieval-Augmented Generation (RAG)

3. AI/LLM agents

4. Multi-agent architectures

5. Conversational AI

6. AI copilots

7. Enterprise search

8. Document intelligence

9. Summarization and classification

10. Knowledge assistants

11. Workflow automation

- Develop reference architectures, design patterns, standards, and reusable components for GenAI applications.

- Evaluate build-versus-buy decisions for AI platforms and capabilities.

- Design solutions that support scalability, reliability, performance, observability, and cost optimization.

- Ensure architecture decisions align with enterprise technology strategy and long-term platform objectives.

- Establish architectural principles for production-grade GenAI development and deployment.

- Evaluate and select appropriate foundation models based on use case requirements.

- Assess commercial and open-source LLMs for:

1. Accuracy

2. Context-window requirements

3. Latency

4. Cost

5. Security

6. Data privacy

7. Availability

8. Fine-tuning capabilities

9. Multilingual support

- Design model-routing and model-selection strategies where multiple LLMs are required.

- Evaluate hosted APIs, managed foundation models, and self-hosted/open-source models.

- Establish standards for model evaluation, benchmarking, versioning, and lifecycle management.

- Assess opportunities for fine-tuning, instruction tuning, prompt optimization, and retrieval-based approaches.

- Design and implement enterprise-grade RAG architectures.

- Define strategies.