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# Senior AI Engineer

- **Company:** Genesis10
- **Location:** Charlotte, NC, United States
- **Experience:** Expert
- **Salary:** $114,525.0 - $131,165.0
- **Contract:** Temporary contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Profiling, Computer Programming, Continuous Integration, Software Debugging, Software Design Patterns, Programming Tools, Python (Programming Language), OpenAI, Search Technologies, Software Construction, Systems Integration, Pinecone, Qdrant, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Generative AI, Agentic-AI, Git, Pgvector, Containerization, LangSmith, HuggingFace, Performance Monitor, Weaviate, Milvus, Ollama, Asynchronous Programming, Model Context Protocol, Docker
- **Published:** October 29, 2026
- **Apply:** https://www.thejobnetwork.com/job/37aa8585-2fb3-421d-bb0d-b38eff4a05f1/senior-ai-engineer

## About the Role

* Core Programming: Advanced, production-level proficiency in Python (asyncio, design patterns, profiling, and testing)
 * Orchestration & Agent Frameworks: Demonstrated hands-on production experience with one or more of the following:
 + LangChain
 + LangGraph
 + LangSmith
 + Deepagents
 * RAG & Vector Search: Deep working knowledge of RAG pipelines, prompt engineering strategies, and integration with vector databases (e.g., Pinecone, Weaviate, Milvus, Qdrant, or pgvector)
 * LLM Ecosystem: Familiarity with major LLM providers (OpenAI, Anthropic, open-source models via Hugging Face/Ollama) and function-calling / tool-use architectures
 * Software Engineering Best Practices: Experience with Git, CI/CD pipelines, containerization (Docker), and cloud environments (AWS, GCP, or Azure)

## Description

* Architect & Scale RAG Pipelines: Design, implement, and optimize advanced Retrieval-Augmented Generation (RAG) architectures, including custom chunking, hybrid search, embedding optimization, and reranking mechanisms
 * Build Agentic Workflows: Develop complex, stateful multi-agent systems and autonomous workflows using frameworks such as LangGraph and Deepagents
 * Orchestrate & Monitor: Utilize LangChain for robust LLM integration and LangSmith for tracing, evaluation, debugging, and performance monitoring of production LLM pipelines
 * Advanced Python Engineering: Write clean, modular, highly efficient, and maintainable Python code adhering to modern standards (asynchronous programming, strict typing, and robust error handling)
 * System Integration: Connect LLM agents and workflows with external data sources, enterprise APIs, vector databases, and developer tooling (including Model Context Protocol integrations where applicable)
 * Performance & Cost Optimization: Optimize token usage, latency, and operational costs across LLM-driven applications without compromising response quality or accuracy