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.