# Daman — AI Architect

- Generated: 2026-09-10 07:18:30 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4464110934/
- Provider: LinkedIn
- Posted: approximately 2026-09-09 06:18 PM EDT (from '13 hours ago')
- Applicants: 38 applicants
- Work model/location: 100% remote — United States
- Compensation: Not disclosed; long-term contract
- Travel: Not disclosed
- Positioning track: Technical IC
- Fit outcome: FAIL — 91%; Keith explicitly overrode the fit gate and requested application materials

## Direct-match strengths

Production agentic AI, multi-agent orchestration, LLMs, RAG, Python, LangChain/LangGraph, vector stores, evaluation, governance, observability, APIs, AWS, and technical mentorship.

## Hard or material gaps

Original hard gap was undisclosed compensation for a non-Director/VP/Chief title. AutoGen, CrewAI, and Semantic Kernel are not claimed; equivalent LangChain/LangGraph experience is direct. Keith explicitly overrode the FAIL on September 10, 2026.

## Evidence map

1. Production GenAI and agentic systems — direct recent implementation evidence from AssistX.
2. RAG, vector retrieval, Python, APIs, and cloud architecture — direct hands-on evidence.
3. Evaluation, governance, observability, security, and production operations — direct evidence.
4. Enterprise stakeholder translation and technical leadership — direct AWS, NorthBay, and product-platform evidence.
5. Remaining gaps — accurately identified above and not inserted into the resume.

## Keyword diagnostic

Very strong agentic-AI alignment; resume emphasizes supported equivalents and does not claim unsupported named frameworks.

## Full normalized job description

Job Title:
AI Architect
Location:
100% Remote
Job Type:
Long-term Contract
Position Overview
We are seeking an experienced AI Architect with strong expertise in Agentic AI systems to design and implement intelligent, autonomous AI solutions that can reason, plan, and execute complex tasks. The ideal candidate will have deep experience in large language models (LLMs), multi-agent systems, orchestration frameworks, and AI infrastructure, along with the ability to translate business requirements into scalable AI architectures.
This role will involve architecting end-to-end AI solutions, guiding engineering teams, and ensuring reliable deployment of agent-based AI systems across enterprise environments.
Key Responsibilities
Design and architect Agentic AI systems capable of autonomous reasoning, decision-making, and task execution.
Build and implement multi-agent frameworks that collaborate to solve complex workflows.
Define architecture for LLM-powered applications, including prompt orchestration, tool usage, and memory management.
Integrate AI agents with enterprise systems, APIs, and data platforms.
Lead the design of AI pipelines, orchestration layers, and evaluation frameworks.
Establish best practices for AI governance, safety, observability, and monitoring.
Collaborate with product managers, data scientists, and engineering teams to translate business requirements into AI-driven solutions.
Guide teams in deploying scalable AI solutions on cloud platforms such as Azure, AWS, or GCP.
Evaluate and adopt emerging technologies in autonomous AI, reasoning systems, and agent frameworks.
Provide technical leadership and mentorship to AI/ML engineers.
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field.
8+ years of experience in software engineering, machine learning, or AI architecture.
3+ years of experience working with LLM-based systems and generative AI solutions.
Strong experience designing Agentic AI or multi-agent architectures.
Proficiency with Python and AI/ML frameworks.
Hands-on experience with LLM orchestration frameworks such as LangChain, AutoGen, CrewAI, Semantic Kernel, or similar.
Experience working with vector databases (Pinecone, Weaviate, FAISS, etc.).
Knowledge of RAG architectures, prompt engineering, tool integration, and memory management in AI agents.
Experience deploying AI workloads in cloud environments (Azure, AWS, or GCP).
Strong understanding of API design, microservices architecture, and distributed systems.
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## Artifact metadata

- Resume: https://bit.ly/46NAZzR
- Cover letter: https://bit.ly/4gJXOKL
- Validation: PASS — 2-page resume (938 words), 1-page cover letter (209 words); PDF geometry, bounds, annotations, links, and visual pages verified.
- Google Drive used: No