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Location: hybrid role – 4 days onsite @ Irving, TX – ( No – CPT, OPT, H1 transfer visa please)
Duration: Long-term contract
Title: AI Developer
What You’ll Work On
Prompt Engineering System
YAML/Markdown-based prompt loader with dynamic filtering, substitutions, and routing
AI Chat Agent
Visual Studio 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:
10 years of software engineering experience.
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:
Visual Studio Code Extension Development
Visual Studio Code APIs & Chat Participant API
Language Model API integration
VSIX packaging
Data Formats:
YAML
Markdown
JSON
Location: hybrid role – 4 days onsite @ Irving, TX – ( No – CPT, OPT, H1 transfer visa please)
Duration: Long-term contract
Title: AI Developer
What You’ll Work On
Prompt Engineering System
YAML/Markdown-based prompt loader with dynamic filtering, substitutions, and routing
AI Chat Agent
Visual Studio 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:
10 years of software engineering experience.
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:
Visual Studio Code Extension Development
Visual Studio Code APIs & Chat Participant API
Language Model API integration
VSIX packaging
Data Formats:
YAML
Markdown
JSON