Check out the role overview below If you are confident you have got the right skills and experience, apply today.
We have an immediate Openings with Our Direct Client for a Long-term contract position
Job Title: AI Engineer – AI Modernization Factory
Location: Irving TX
Duration – 12 months
Role Summary
AI Engineer to implement the design, development, and evolution of the VZ Application AI Modernization Factory—an AI-powered platform that automates and accelerates the modernization of large-scale enterprise legacy applications.
This VS Code extension-based solution leverages Large Language Models (LLMs), knowledge graphs, adaptive questioning, and code generation to transform legacy Java/Oracle enterprise systems into modern architectures such as NSA.
The role involves driving the end-to-end technical vision of the AI factory—from intelligent source code analysis to automated artifact generation—while collaborating closely with modernization teams, platform engineers, and AI specialists to continuously improve throughput, accuracy, and coverage.
Key Responsibilities
GenAI Engineering
Implement the prompt engineering system — structured
YAML and Markdown prompt templates with dynamic placeholder substitution, priority filtering, category routing, and multi-instance LightRAG targeting.
Build and refine the Adaptive Questioning Framework
— an LLM-driven recursive questioning engine with configurable probing levels, depth control, SQL indirection detection, and migration-critical validation guarantees.
Implement and maintain MCP server integration with tools for vector store operations (upsert, search), Neo4j graph database queries, file metadata lookup. xsgimln
What You’ll Work On
Prompt Engineering System:
YAML/Markdown prompt loader with dynamic filtering, placeholder substitution, and multi-instance routing
AI Chat Agent:
VS Code chat participant with guided modernization workflows
Adaptive Questioning Engine:
LLM-driven recursive analysis with configurable depth, probing levels, SQL indirection detection, and migration-critical enforcement
Knowledge Graph Integration:
LightRAG + Neo4j pipeline for context-aware legacy application analysis
Artifact Generation Pipeline:
Automated LLD, code instructions, and test instructions generation aligned with enterprise coding standards
MCP Server & Tools:
Vector store, graph database, and file metadata tools
Late Chunking & Embedding:
Semantic context retrieval pipeline reducing LLM token usage
Python Vector Services:
High-performance embedding and similarity operations with zero dependency
Technical Skills
Languages: TypeScript, Python, SQL
Runtime: Node.js, Python
Gen AI: Prompt engineering, token optimization, multi-model orchestration, and RAG architectures, MCP
Platform: VS Code Extension development - API, Chat Participant API, Language Model API, VSIX packaging
Data Formats: YAML, Markdown, JSON
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