# HMG AMERICA LLC — Generative AI Architect

- Generated: 2026-09-22 07:28:43 PM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4469026153/
- Provider: LinkedIn
- Posted: approximately 2026-09-22 12:17 PM EDT (from '5 hours ago' at original capture)
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp when available
- Applicants: 39 applicants
- Work model/location: Remote — United States
- Compensation: Not disclosed; Keith explicitly overrode the original title/compensation failure
- Travel: As needed; no percentage disclosed
- Positioning track: Technical IC
- Original fit outcome: FAIL — 88%
- Override: Keith explicitly authorized resume and cover-letter generation on September 22, 2026

## Direct-match strengths

Enterprise GenAI architecture, LLMs, agents, RAG, vectors, multi-agent systems, OpenAI, Claude, Bedrock, Vertex AI, Python, LangChain/LangGraph, containers, CI/CD, MLOps, governance, evaluation, guardrails, MCP, and client-facing architecture.

## Hard or material gaps

Original hard gate retained: compensation is undisclosed for an Architect title. Azure OpenAI, CrewAI, AutoGen, Pinecone, Weaviate, FAISS, and ChromaDB are not source-supported; the resume uses supported alternatives only. Unbounded travel also requires confirmation within Keith's 11% maximum. Keith explicitly overrode the FAIL on September 22, 2026.

## Evidence map

1. Enterprise GenAI architecture (weight 3, evidence 3/3) — Direct reusable production AI-platform architecture.
2. LLMs, agents, RAG, and vectors (weight 3, evidence 3/3) — Direct current implementation.
3. Python and orchestration frameworks (weight 3, evidence 3/3) — Direct Python, LangChain, and LangGraph evidence.
4. Cloud-native AI (weight 2, evidence 3/3) — Direct AWS Bedrock and Vertex AI; Azure is unsupported.
5. MLOps, evaluation, governance, and guardrails (weight 2, evidence 3/3) — Direct production controls and regulated governance.
6. MCP and multi-agent systems (weight 2, evidence 3/3) — Direct source-supported evidence.
7. Qualifying compensation (weight 3, evidence 0/3) — Undisclosed for an Architect title; explicitly overridden.

## Keyword diagnostic

Very strong enterprise GenAI architecture fit; override preserves compensation, travel, and unsupported named-tool caveats.

## Full normalized job description

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.

## Artifact metadata

- Resume: https://bit.ly/4rqZCf6
- Cover letter: https://bit.ly/4hhFNnr
- Validation: PASS — 2-page resume (934 words), 1-page cover letter (193 words); PDF geometry, bounds, annotations, links, and visual pages verified.
- Google Drive used: No