# Envision Technology Solutions — GenAI Architect

- Generated: 2026-09-10 07:18:30 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4465347429/
- Provider: LinkedIn
- Posted: approximately 2026-09-09 07:18 PM EDT (from '12 hours ago')
- Applicants: 33 applicants
- Work model/location: Remote — United States
- Compensation: Not disclosed; contract
- Travel: Not disclosed
- Positioning track: Technical IC
- Fit outcome: FAIL — 93%; Keith explicitly overrode the fit gate and requested application materials

## Direct-match strengths

LLMs, RAG, agents, tools, embeddings, PGVector, evaluation, guardrails, Python, LangChain/LangGraph, OpenAI, Bedrock, Docker, Kubernetes, CI/CD, IAM, APIs, and AWS.

## Hard or material gaps

Original hard gap was undisclosed compensation. Azure-specific services and several named vector/orchestration tools are not claimed, but supported equivalents are 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

Best technical alignment of the original batch; resume uses only source-supported platforms and equivalent technologies.

## Full normalized job description

Hi,
Hope you are doing well!
Please find the job requirement below. If your experience aligns with the role and you are interested in exploring this opportunity, please share your updated resume.
Job Title: GenAI Architect
Location: Remote
Type: Contract
Job Summary
We are seeking an experienced GenAI Architect to design and lead the implementation of enterprise-grade Generative AI solutions. The ideal candidate will have strong experience in LLMs, RAG, AI agents, cloud platforms, AI/ML architecture, and enterprise integrations.
The architect will work closely with business stakeholders, product teams, data scientists, software engineers, and cloud teams to define AI strategies, design scalable solutions, establish AI governance, and guide implementation from proof of concept through production.
Key Responsibilities
Design end-to-end Generative AI architectures for enterprise applications.
Develop solutions using LLMs, RAG, prompt engineering, AI agents, and agentic workflows.
Evaluate and select LLMs such as OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, and open-source models.
Design scalable RAG pipelines including document ingestion, chunking, embeddings, vector search, retrieval, reranking, and response generation.
Architect AI agent and multi-agent systems using tool calling, function calling, memory, orchestration, and workflow automation.
Integrate GenAI solutions with enterprise applications, APIs, databases, and business systems.
Design solutions using vector databases such as Pinecone, Weaviate, Milvus, Qdrant, or pgvector.
Establish AI security, governance, privacy, responsible AI, and guardrails.
Address hallucination, prompt injection, data leakage, model evaluation, grounding, and AI safety concerns.
Design LLMOps/MLOps processes for model deployment, monitoring, evaluation, and continuous improvement.
Work with cloud platforms such as AWS, Azure, and GCP to build scalable AI solutions.
Create architecture diagrams, technical designs, POCs, prototypes, and implementation roadmaps.
Mentor engineers and provide technical leadership throughout the development lifecycle.
Collaborate with senior leadership to identify and prioritize high-value GenAI use cases.
Required Skills
8+ years of experience in software engineering, solution architecture, AI/ML, or related technology roles.
3+ years of hands-on experience with Generative AI/LLMs.
Strong knowledge of:
LLMs
RAG
Prompt Engineering
AI Agents / Agentic AI
Embeddings
Vector Databases
Function/Tool Calling
Model Evaluation
AI Guardrails
Strong programming experience with Python and/or Java.
Experience with frameworks such as:
LangChain
LlamaIndex
Semantic Kernel
AutoGen
LangGraph
Experience with OpenAI/Azure OpenAI APIs or equivalent LLM platforms.
Experience with AWS, Azure, or GCP AI/cloud services.
Strong understanding of REST APIs, microservices, event-driven architecture, and enterprise integration.
Experience with databases such as PostgreSQL, MongoDB, SQL Server, and vector databases.
Knowledge of Docker, Kubernetes, CI/CD, Terraform, and cloud-native architectures.
Strong understanding of security, IAM, encryption, data privacy, and enterprise AI governance.

## Artifact metadata

- Resume: https://bit.ly/46VMg0Z
- Cover letter: https://bit.ly/4yy1cOX
- Validation: PASS — 2-page resume (927 words), 1-page cover letter (210 words); PDF geometry, bounds, annotations, links, and visual pages verified.
- Google Drive used: No