AI Engineer – AI Modernization Factory

Hi

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.

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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