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Gen AI Architect

net2source (n2s) United State
Remote
This Job is No Longer Active This position is no longer accepting applications
AI Summary

Design and implement scalable multi-agent orchestrated systems leveraging LLMs, LangChain/LangGraph, ACP, and RAG. Lead teams through implementation and drive iterative measure-improve-ship cadence. Collaborate with stakeholders to manage expectations and keep delivery on track.

Key Highlights
Rapidly assess customer landscape
Define and own end-to-end solution and technical architecture
Lead team hands-on through implementation
Key Responsibilities
Assess customer landscape
Design and implement scalable multi-agent orchestrated systems
Lead team hands-on through implementation
Collaborate with stakeholders
Technical Skills Required
Python Data Ingestion Data Pipeline Vector DB Azure AI Foundry Azure AI Service LLMs LangChain/LangGraph ACP RAG
Benefits & Perks
100% Remote
W2 only

Job Description


W2 only


Job title: Gen AI Architect

**100% Remote**

Experience : 14+ years


Required Skills:

1. Architecting, solutioning, and implementing scalable multi-agent orchestrated systems leveraging LLMs, LangChain/LangGraph, ACP, and RAG, with strong capabilities in agent design patterns, workflow orchestration, context/memory engineering, prompt and tool integration, and enterprise-grade observability, governance, and reliability

2. Python, Data Ingestion, Data Pipeline, Vector DB

3. Azure AI Foundry , Azure AI Service (or Similar services)


Job Summary:

1) Rapidly assess the customer landscape, design and implementation by understanding their processes, systems, data, and pain points to identify and implement high-impact GenAI/agentic opportunities.

2) Define and own the end-to-end solution and technical architecture covering model selection, agent design, data ingestions, integration, services, guardrails, evaluation framework, and cost/security posture.

3) Lead the team hands-on through implementation, setting engineering and prompt-engineering standards, unblocking hard problems, and driving an iterative measure-improve-ship cadence.

4) Collaborate with business, IT, security, and engineering stakeholders to manage expectations, communicate risks, align on goals, and keep delivery on track.

5) Own end-to-end delivery and adoption, instrument success metrics, and establish a post-go-live operating model that sustains measurable productivity gains in production.


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