AI Architect (Agentic AI, MCP, RAG)

Envision Technology Solutions; Dearborn (MI); Sur place; USD 170 000 - 260 000; Plein temps

Résumé du poste

Envision Technology Solutions seeks an experienced AI Architect to lead design and implementation of Agentic AI solutions that automate and optimize enterprise workflows. You will shape architecture across LLMs, RAG, and MCP-enabled platforms, collaborating with stakeholders and engineering teams to drive scalable, secure, and observable AI systems.

The role requires deep software engineering background, hands-on Python development, expertise in multi-agent orchestration, and a track record

Qualifications

• 15+ years of software engineering, data engineering, or enterprise application development experience.
• 4+ years of experience architecting enterprise-scale AI, analytics, data, or cloud solutions.
• Hands-on experience with Agentic AI architectures and autonomous workflow design.
• Experience building solutions using Large Language Models (LLMs).
• Strong knowledge of Retrieval Augmented Generation (RAG) implementations.
• Experience with MCP (Model Context Protocol) and enterprise context management frameworks.
• Proficiency in Python and AI application development.
• Hands-on experience with PySpark and large-scale data processing.
• Experience with vector databases, embeddings, semantic search, and knowledge retrieval systems.
• Strong understanding of cloud-native architectures and distributed systems.
• Experience designing API-first and event-driven architectures.
• Excellent communication and stakeholder management skills.

Responsabilités

• Partner with business and technology organizations to understand current operational challenges and manual root cause analysis processes.
• Assess existing workflows and identify opportunities for AI-driven automation and intelligent decision support.
• Architect enterprise-scale Agentic AI platforms and multi-agent orchestration frameworks.
• Design AI systems leveraging LLMs, RAG, Knowledge Graphs, Vector Databases, and MCP-based integrations.
• Define end-to-end architecture for autonomous investigation, diagnosis, recommendation, and remediation workflows.
• Create reusable AI patterns, reference architectures, governance standards, and best practices.
• Lead architecture reviews, technology evaluations, and AI platform roadmaps.
• Collaborate with data engineers, platform teams, application developers, and business stakeholders to deliver scalable solutions.
• Implement AI observability, monitoring, security, compliance, and responsible AI practices.
• Guide development teams on prompt engineering, reasoning frameworks, agent orchestration, memory management, and context handling.
• Evaluate emerging AI technologies and recommend adoption strategies aligned with business objectives.
• Drive AI strategy and architecture discussions with executive leadership and enterprise stakeholders.

Connaissances

Agentic AI Architecture

LLM & RAG

Python & PySpark

Multi-Agent Orchestration

Enterprise AI Architecture

Knowledge Graphs

Vector Databases

Outils

LangChain

Semantic Kernel

AutoGen

CrewAI

LangGraph

Description du poste

Job Title : AI Architect (Agentic AI, MCP, RAG)

Location : Dearborn, MI (Onsite)

Contract : Long Term Contract

Job Summary:

We are seeking a highly experienced AI Architect to lead the design and implementation of next-generation Agentic AI solutions that transform manual problem-solving and root cause analysis processes across the enterprise. This role will collaborate with business stakeholders, engineering teams, operations groups, and leadership across multiple organizations to assess existing workflows, identify automation opportunities, and architect intelligent AI-driven solutions that improve operational efficiency, accelerate decision-making, and reduce resolution times.

The ideal candidate combines deep software engineering expertise with strong architecture experience in Agentic AI, LLMs, RAG, MCP, and enterprise-scale AI platforms, along with hands-on proficiency in Python-based development.

Key Responsibilities

• Partner with business and technology organizations to understand current operational challenges and manual root cause analysis processes.
• Assess existing workflows and identify opportunities for AI-driven automation and intelligent decision support.
• Architect enterprise-scale Agentic AI platforms and multi-agent orchestration frameworks.
• Design AI systems leveraging LLMs, RAG, Knowledge Graphs, Vector Databases, and MCP-based integrations.
• Define end-to-end architecture for autonomous investigation, diagnosis, recommendation, and remediation workflows.
• Create reusable AI patterns, reference architectures, governance standards, and best practices.
• Lead architecture reviews, technology evaluations, and AI platform roadmaps.
• Collaborate with data engineers, platform teams, application developers, and business stakeholders to deliver scalable solutions.
• Implement AI observability, monitoring, security, compliance, and responsible AI practices.
• Guide development teams on prompt engineering, reasoning frameworks, agent orchestration, memory management, and context handling.
• Evaluate emerging AI technologies and recommend adoption strategies aligned with business objectives.
• Drive AI strategy and architecture discussions with executive leadership and enterprise stakeholders.

Required Qualifications

• 15+ years of overall software engineering, data engineering, or enterprise application development experience.
• 4+ years of experience architecting enterprise-scale AI, analytics, data, or cloud solutions.
• Strong hands-on expertise in Agentic AI architectures and autonomous workflow design.
• Experience building solutions using Large Language Models (LLMs).
• Strong knowledge of Retrieval Augmented Generation (RAG) implementations.
• Experience with MCP (Model Context Protocol) and enterprise context management frameworks.
• Proficiency in Python and AI application development.
• Hands-on experience with PySpark and large-scale data processing.
• Experience with vector databases, embeddings, semantic search, and knowledge retrieval systems.
• Strong understanding of cloud-native architectures and distributed systems.
• Experience designing API-first and event-driven architectures.
• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Experience with Azure OpenAI, OpenAI, Anthropic, Gemini, or similar AI ecosystems.
• Experience with LangChain, Semantic Kernel, AutoGen, CrewAI, LangGraph, or comparable agent frameworks.
• Knowledge of MLOps, LLMOps, AI governance, and responsible AI practices.
• Experience with Databricks, Spark, Azure AI Services, Kubernetes, and GCP cloud platform engineering.
• Experience developing AI-powered operational intelligence and root cause analysis solutions.
• Automotive, manufacturing, or enterprise operations domain experience is highly desirable.
• Experience leading enterprise architecture initiatives and mentoring engineering teams.

Top Skills

• 1. Agentic AI Architecture
• 2. LLM, RAG, MCP
• 3. Python & PySpark
• 4. Multi-Agent Systems & AI Orchestration