AI/ML Software Engineer

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Title: AI/ML Software Engineer

Duration: 5 Months (3 Years Extension)

Location: Austin, Texas 78751. The working position is Hybrid - On Site and Telework. (Need Locals)

Position will be 3 days remote with 2 days (Tues & Wed) required to be onsite at the location listed above.

Summary

Researching, designing, implementing and managing software programs. Testing and evaluating new programs. Working closely with other developers, UX designers, business and systems analysts.

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.

Required Skills

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

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

4 Years Of Required Strong Experience In Context Engineering

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

4 Years of Required Experience implementing RAG architectures using vector databases

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

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

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

Preferred Skills

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

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

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