AI Agent Engineer (In-person Interview)

Qualifications

• 4 Years of experience in AI/ML engineering or advanced data science

• 4 Years of Proven track record of building and deploying production-grade autonomous agents

• 4 Years of Strong experience in context engineering

• 4 Years of Deep experience with LangChain, LangGraph, CrewAI, or AutoGPT

• 4 Years of Experience implementing RAG architectures using vector databases

• 4 Years of Proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI)

• 4 Years of Experience integrating LLMs via APIs Knowledge of AI governance, model lifecycle management, and evaluation

• 4 Years of Experience implementing and extending the Model Context Protocol (MCP) to provide LLMs with secure, standardized access to local and remote data sources Experience implementing AI guardrails, content filtering, and safety controls

• 4 Years of Understanding of data privacy and handling of sensitive data (PII/PHI)

• 2 Years of Experience building multi-agent or autonomous agentic workflows

• 2 Years of Experience optimizing LLM cost, token usage, and performance

• 2 Years of Familiarity with enterprise AI deployment patterns and scalability considerations

• 9 more items(s)

Responsibilities

• AI Agent Engineer Designs and develops AI-driven agentic solutions, including autonomous workflows and Retrieval-Augmented Generation (RAG) systems, to enhance productivity, automate processes, and support intelligent decision-making with a focus on governance, security, and cost efficiency

More job highlights

Job description

Job title: AI Agent Engineer

Location: Austin, TX (Hybrid)

The primary work location(s) will be at 4601 W Guadalupe St, Austin, TX 78751.

Duration: 04+ Months

Job number: 529601670

Due date: 04/09/2026

Interview: In person only (No exception)

Some hybrid work – determined by the hiring manager

Job Discription:

AI Agent Engineer Designs and develops AI-driven agentic solutions, including a...utonomous workflows and Retrieval-Augmented Generation (RAG) systems, to enhance productivity, automate processes, and support intelligent decision-making with a focus on governance, security, and cost efficiency

Required Skills:

4 Years of experience in AI/ML engineering or advanced data science

4 Years of Proven track record of building and deploying production-grade autonomous agents.

4 Years of Strong experience in context engineering

4 Years of Deep experience with LangChain, LangGraph, CrewAI, or AutoGPT.

4 Years of Experience implementing RAG architectures using vector databases

4 Years of Proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI)

4 Years of Experience integrating LLMs via APIs Knowledge of AI governance, model lifecycle management, and evaluation

4 Years of Experience implementing and extending the Model Context Protocol (MCP) to provide LLMs with secure, standardized access to local and remote data sources Experience implementing AI guardrails, content filtering, and safety controls

4 Years of Understanding of data privacy and handling of sensitive data (PII/PHI)

Preferred Skills:

2 Years of Experience building multi-agent or autonomous agentic workflows

2 Years of Experience optimizing LLM cost, token usage, and performance

2 Years of Familiarity with enterprise AI deployment patterns and scalability considerations

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