14538 – AI Engineer (hybrid) – Austin, TX

Start Date: ASAP
Type: Temporary Project
Estimated Duration: 12 months with possible extensions
Work Setting: Hybrid - 3 days remote with 2 days (Tues & Wed) required to be onsite

Required:
• Availability to work 100% of the time at the Client’s site in Austin, TX (required);
• Experience in AI/ML engineering or advanced data science (4 years);
• Experience building and deploying production-grade autonomous agents (4 years);
• Experience in context engineering (4 years);
• Experience with LangChain, LangGraph, CrewAI, or AutoGPT (4 years);
• Experience implementing RAG architectures using vector databases (4 years);
• Experience in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI) (4 years);
• Experience integrating LLMs via APIs Knowledge of AI governance, model lifecycle management, and evaluation (4 years);
• 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);
• Experience in data privacy and handling of sensitive data (PII/PHI) (4 years).

Preferred:
• Experience building multi-agent or autonomous agentic workflows (2 years);
• Experience optimizing LLM cost, token usage, and performance (2 years);
• Experience with enterprise AI deployment patterns and scalability considerations (2 years).

Responsibilities include but are not limited to the following:
• Design and develop AI‑driven agentic solutions, including autonomous workflows and RAG systems, to enhance productivity and support intelligent decision‑making.
• Research, design, implement, and manage software programs that integrate LLMs, vector databases, and external systems in secure, scalable ways.
• Implement and extend Model Context Protocol (MCP) integrations to provide standardized, governed access to local and remote data sources for AI agents.
• Develop and enforce AI guardrails, content filtering, safety controls, and governance mechanisms to ensure compliant use of sensitive data (PII/PHI).
• Optimize LLM usage, including prompt and context engineering, cost management, token efficiency, and performance tuning of agentic workflows.
• Collaborate closely with other developers, UX designers, business and systems analysts to refine requirements, design solutions, and support end‑to‑end delivery.
• Test, evaluate, and iterate on new AI‑driven programs and autonomous agents, monitor production behavior, and address issues including after‑hours or weekend work as requested by the client.