AI Engineer – AI Modernization Factory

Dice is the leading career destination for tech experts at every stage of their careers. Our client, InfoVision, Inc., is seeking the following. Apply via Dice today! 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 If interested, Please share below details with update resume: Full Name: Phone: E-mail: Rate: Location: Visa Status: Availability: SSN (Last 4 digit): Date of Birth: LinkedIn Profile: Availability for the interview: Availability for the project: