Gen AI Architect

Job Title: Gen AI Architect
Location: Remote (USA) – Travel as Needed
Employment Type: Full-Time
About the Role
We are seeking an experienced Generative AI Architect (Level 4) to lead the design, architecture, and implementation of enterprise-scale AI solutions. The ideal candidate will have deep expertise in Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), AI orchestration frameworks, and cloud-native architectures.
Key Responsibilities
Design and implement enterprise-grade Generative AI and Agentic AI solutions.
Architect scalable AI platforms leveraging LLMs, RAG, vector databases, and multi-agent systems.
Lead solution design using OpenAI, Claude, Gemini, Llama, and other foundation models.
Develop AI architectures on AWS, Azure, or GCP with a focus on security, scalability, and performance.
Define best practices for prompt engineering, model evaluation, AI governance, and Responsible AI.
Collaborate with business stakeholders to translate business requirements into AI-driven solutions.
Lead technical teams through architecture reviews and solution delivery.
Establish MLOps/LLMOps practices for deployment, monitoring, and model lifecycle management.
Mentor AI engineers and development teams on GenAI technologies.
Required Qualifications
Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field.
10+ years of overall IT experience with software engineering, cloud architecture, or AI/ML.
3+ years of hands-on Generative AI architecture experience.
Strong expertise in Python and AI development frameworks.
Experience with:
OpenAI, Claude, Gemini, Llama
LangChain, LangGraph, CrewAI, AutoGen
RAG architectures and Vector Databases (Pinecone, Weaviate, FAISS, ChromaDB)
AWS Bedrock, Azure OpenAI, Google Vertex AI
Docker, Kubernetes, CI/CD, MLOps/LLMOps
Strong understanding of AI governance, security, compliance, and Responsible AI.
Excellent communication and client-facing skills.
Preferred Qualifications
Experience designing Agentic AI and multi-agent architectures.
Knowledge of MCP (Model Context Protocol) and AI agent ecosystems.
Experience with AI observability, evaluation frameworks, and guardrails.
Consulting or customer-facing architecture experience.
Relevant cloud certifications preferred.