Generative AI Engineer

Job Title: Agentic AI Engineer / Developer

Location: Richardson, TX / Charlotte, NC (Hybrid/Onsite)

Interview Process:

• Round 1: Virtual Interview
• Final Round: Mandatory In-Person Client Interview

Job Description

We are seeking an experienced Agentic AI Engineer to design, develop, and deploy intelligent AI agents capable of autonomous reasoning, planning, and task execution. The ideal candidate will have strong experience with LLMs, agent orchestration frameworks, RAG pipelines, and cloud-native AI solutions.

Required Skills

• 15+ years of software development experience with Python
• 2+ years of hands-on experience building Agentic AI solutions
• Strong experience with LangChain, LangGraph, CrewAI, AutoGen, or Google ADK
• Experience integrating LLMs such as GPT-4, Gemini, Claude, or Llama
• Hands-on experience with Retrieval-Augmented Generation (RAG)
• Experience with vector databases such as Pinecone, Weaviate, ChromaDB, Milvus, or FAISS
• Strong understanding of prompt engineering and AI agent workflows
• Experience building REST APIs using FastAPI or Flask
• Knowledge of cloud platforms (AWS, Azure, or GCP) and AI services
• Experience with Git, CI/CD, Docker, and Kubernetes
• Strong debugging, problem-solving, and communication skills

Preferred Skills

• Experience with MCP (Model Context Protocol)
• Knowledge of AI evaluation frameworks such as LangSmith or Ragas
• Experience with multi-agent systems and workflow orchestration
• Familiarity with Neo4j or GraphRAG
• Experience with MLOps and model deployment
• Exposure to enterprise AI governance and security best practices

Responsibilities

• Design and develop autonomous AI agents for enterprise use cases
• Build RAG pipelines using vector databases and enterprise data sources
• Integrate LLMs with internal APIs, databases, and third-party systems
• Develop multi-agent workflows for reasoning, planning, and task execution
• Optimize prompts, memory management, and agent performance
• Deploy scalable AI applications on cloud infrastructure
• Collaborate with cross-functional teams to deliver production-ready AI solutions
• Monitor, evaluate, and continuously improve AI agent performance