AI Engineer – AI Modernization Factory

AI Engineer – AI Modernization Factory Role Summary We are seeking an AI Engineer to design, develop, and evolve 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 automated code generation to transform legacy Java/Oracle 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 1. GenAI Engineering • Implement a prompt engineering system using structured YAML and Markdown templates, including: • Dynamic placeholder substitution • Priority filtering • Category-based routing • Multi-instance LightRAG targeting • Build and enhance the Adaptive Questioning Framework, featuring: • LLM-driven recursive questioning • Configurable probing depth and levels • SQL indirection detection • Migration-critical validation guarantees • Implement and maintain MCP server integrations, including: • Vector store operations (upsert, search) • Neo4j graph database queries • File metadata retrieval 1. Platform Development • Design, build, and maintain a VS Code extension (TypeScript/Node.js), including: • Chat participant integration • Command handlers • Guided conversational workflows • Design and implement a multi-stage modernization pipeline: • Application selection • Module-level targeted analysis • Adaptive deep-dive questioning • LLD (Low-Level Design) generation • Code instruction generation • Test instruction generation • Implementation guidance • Develop and evolve a modular extension architecture, including: • Services layer: LLM, session, file, user, adaptive questioning • Handlers: Chat participant, conversations, APIs, workflows • Utilities: Embeddings, token management, error tracking, SQL detection • UI components: Buttons, markdown rendering, progress indicators • Implement a tiered error-handling framework: • Early-stage failure: Stop execution and prompt connectivity diagnostics • Mid-stage failure: Pause and auto-retry with exponential backoff • Late-stage failure: Continue with partial results • Error classification: NETWORK, AUTH, SERVER, TIMEOUT, UNKNOWN • Maintain build and packaging pipelines, including: • TypeScript strict compilation • Bundling • Automated VSIX packaging • Integrate the VS Code extension with LightRAG services, including: • Connection lifecycle management • Endpoint targeting and routing • Contextual retrieval of legacy code artifacts • Collaborate with: • LightRAG platform teams on ingestion pipelines and retrieval quality • AI engineering peers on shared architecture and enhancements 1. Python Services • Maintain Python-based services for vector operations, including: • Cosine similarity • Batch similarity computation • JSON-based TypeScript ↔ Python subprocess interoperability • Automatic TypeScript fallback on failures • Manage embedding pipelines, including: • External embedding API integrations • Batch processing • Exponential backoff retry strategies • Configurable batching What You’ll Work On • Prompt Engineering System • YAML/Markdown-based prompt loader with dynamic filtering, substitutions, and routing • AI Chat Agent • VS Code chat participant enabling guided modernization workflows • Adaptive Questioning Engine • Recursive LLM-driven analysis with depth control and migration enforcement • Knowledge Graph Integration • LightRAG + Neo4j pipeline for context-aware analysis • Artifact Generation Pipeline • Automated generation of: • Low-Level Designs (LLD) • Code instructions • Test instructions • MCP Server & Tools • Integration with vector stores, graph databases, and file metadata services • Late Chunking & Embedding • Efficient semantic retrieval to optimize token usage • Python Vector Services • High-performance similarity and embedding computation Technical Skills Languages: TypeScript, Python, SQL Runtime: Node.js, Python GenAI & AI Systems: • Prompt engineering • Token optimization • Multi-model orchestration • Retrieval-Augmented Generation (RAG) • Model Context Protocol (MCP) Platform Development: • VS Code Extension Development • VS Code APIs & Chat Participant API • Language Model API integration • VSIX packaging Data Formats: • YAML • Markdown • JSON