# Envision Technology Solutions — Gen AI Architect

- Generated: 2026-09-10 06:48:29 AM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4464122932/
- Provider: LinkedIn
- Posted: approximately 2026-09-09 05:48 PM EDT (from '13 hours ago')
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp
- Applicants: Be among the first 25 applicants
- Work model/location: United States; remote status not verified in the role text or accessible listing metadata
- Compensation: Not disclosed
- Travel: Not disclosed
- Positioning track: Technical IC
- Fit outcome: FAIL — 87%

## Direct-match strengths

GenAI architecture, LLMs, RAG, multi-agent systems, LangChain/LangGraph, PGVector, knowledge graphs, Python, TensorFlow, AWS, APIs, evaluation, observability, governance, security, and production delivery.

## Hard or material gaps

Hard compensation-policy failure for a non-Director/VP/Chief title, plus unverified work model. Fine-tuning, LoRA/PEFT, and deep foundation-model internals are required at a level not supported by Keith's evidence.

## Evidence map

1. Enterprise GenAI architecture (weight 3, evidence 3/3) — Direct production platform architecture.
2. Agentic and multi-agent systems (weight 3, evidence 3/3) — Direct agents, orchestration, MCP, and workflow evidence.
3. RAG and knowledge systems (weight 3, evidence 3/3) — Direct embeddings, PGVector, semantic retrieval, and Neptune knowledge graph.
4. Python, APIs, cloud, and distributed systems (weight 3, evidence 3/3) — Direct AWS and production engineering evidence.
5. Evaluation, observability, governance, security (weight 2, evidence 3/3) — Direct IDW and MassMutual evidence.
6. Fine-tuning and LoRA/PEFT (weight 3, evidence 0/3) — Required hands-on depth is unsupported.
7. Compensation and work model (weight 3, evidence 0/3) — Both fail Keith's constraints.

## Keyword diagnostic

Strong applied GenAI overlap; model-training depth, compensation, and work-model verification prevent PASS.

## Full normalized job description

We are seeking a highly experienced
GenAI Architect / Principal AI/ML Hands-On Architect
to lead the design, development, and deployment of next-generation AI solutions. The ideal candidate will have deep expertise in
Generative AI, Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), Multi-Agent Systems, and AI/ML architecture
.
This is a hands-on technical leadership role requiring the ability to translate complex business requirements into scalable, secure, and production-ready AI platforms. The architect will work closely with engineering, product, data, and business teams to define AI strategy, establish architecture standards, and build innovative AI solutions from concept to production.
Key Responsibilities
Define and drive the
Generative AI and AI/ML architecture strategy
across enterprise applications and platforms.
Architect and implement
Agentic AI systems
capable of autonomous reasoning, planning, tool use, decision-making, and task execution.
Design and develop sophisticated
LLM-powered applications
using commercial and open-source foundation models.
Architect enterprise-grade
Retrieval-Augmented Generation (RAG)
solutions, including document ingestion, chunking, embeddings, vector search, retrieval optimization, reranking, and context management.
Design and implement
Multi-Agent Systems
involving specialized AI agents, agent orchestration, communication, collaboration, and workflow execution.
Evaluate and select appropriate
LLMs, embedding models, vector databases, agent frameworks, and AI infrastructure
based on business and technical requirements.
Develop scalable AI/ML architectures covering
model serving, inference, prompt engineering, fine-tuning, evaluation, observability, and governance
.
Build hands-on prototypes, proof-of-concepts, and production-grade AI solutions.
Establish best practices for
LLMOps/MLOps
, including CI/CD, model evaluation, monitoring, tracing, versioning, and lifecycle management.
Design AI systems with strong
security, privacy, responsible AI, governance, and compliance
controls.
Optimize LLM applications for
latency, scalability, reliability, accuracy, and cost
.
Develop and implement
prompt engineering and context engineering
strategies for enterprise AI applications.
Design AI-powered workflows integrating LLMs with
APIs, enterprise systems, databases, knowledge bases, and external tools
.
Lead technical architecture reviews and provide mentorship to AI/ML engineers, software engineers, and data scientists.
Partner with senior stakeholders to identify opportunities where
GenAI and Agentic AI
can deliver measurable business value.
Stay current with advances in
foundation models, AI agents, multimodal AI, reasoning models, RAG architectures, and emerging AI frameworks
.
Required Technical Skills
Generative AI & LLMs
Strong expertise in
Generative AI and Large Language Models
.
Hands-on experience with foundation models such as
GPT, Claude, Gemini, Llama, Mistral, or equivalent models
.
Strong understanding of
transformer architectures, tokenization, embeddings, attention mechanisms, inference, and model limitations
.
Expertise in prompt engineering, structured outputs, function/tool calling, and context management.
Experience with
fine-tuning, LoRA/PEFT, model evaluation, and model selection
.
Agentic AI & Multi-Agent Systems
Deep understanding of
Agentic AI architecture and autonomous AI workflows
.
Experience designing AI agents with:
Planning and reasoning
Tool/API invocation
Memory
State management
Workflow orchestration
Human-in-the-loop capabilities
Guardrails and policy enforcement
Hands-on experience building
Multi-Agent Systems
and agent-to-agent collaboration.
Experience with agent frameworks and orchestration technologies such as
LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or equivalent technologies
.
RAG & Knowledge Systems
Strong hands-on experience designing enterprise
RAG architectures
.
Experience with:
Vector databases
Embedding models
Semantic search
Hybrid search
Reranking
Query rewriting
Metadata filtering
Context optimization
Knowledge graphs
Experience with technologies such as
Pinecone, Weaviate, Milvus, Elasticsearch/OpenSearch, pgvector, or equivalent platforms
.
AI/ML Engineering
Strong programming skills in
Python
and experience with modern AI/ML frameworks.
Experience with
PyTorch, TensorFlow, or equivalent frameworks
.
Strong understanding of traditional
Machine Learning, Deep Learning, NLP, and data engineering concepts
.
Experience building and deploying production-grade AI/ML services using APIs and microservices.
Cloud & Distributed Systems
Strong experience with one or more major cloud platforms:
AWS, Microsoft Azure, or Google Cloud
.
Experience architecting scalable, highly available

## Artifact metadata

- Resume: Not generated because the fit gate returned FAIL.
- Cover letter: Not generated because the fit gate returned FAIL.
- LinkedIn note: Not generated for a FAIL role.
- Google Drive used: No