Description
Description:
The AI/ML Engineer will be responsible for researching, designing, implementing, and managing advanced AI/ML solutions with a focus on agentic architectures. This role involves close collaboration with developers, UX designers, business analysts, and system stakeholders to deliver intelligent, production-grade solutions.The ideal candidate has strong experience with large language models (LLMs), context engineering, and multi-agent systems, along with a deep understanding of AI governance, security, and performance optimization.
Responsibilities:
- Design, develop, and deploy AI-powered agentic solutions and autonomous workflows
- Build and implement Retrieval-Augmented Generation (RAG) systems using vector databases
- Develop and manage scalable AI/ML models and applications using Python and modern AI frameworks
- Integrate large language models (LLMs) via APIs (OpenAI, Hugging Face, Azure AI, etc.)
- Implement context engineering strategies to improve model performance and relevance
- Collaborate with cross-functional teams including developers, analysts, and UX designers
- Ensure AI solutions meet governance, security, and compliance requirements
- Develop and enforce AI guardrails, content filtering, and safety mechanisms
- Implement Model Context Protocol (MCP) for secure data access across systems
- Optimize model performance, token usage, and overall cost efficiency
- Conduct testing, evaluation, and continuous improvement of AI systems
- Support deployment, monitoring, and lifecycle management of AI solutions
Required Skills:
- 4+ years of experience in AI/ML engineering or advanced data science
- Proven experience building and deploying production-grade autonomous agents
- Strong expertise in context engineering
- Hands-on experience with frameworks such as LangChain, LangGraph, CrewAI, or AutoGPT
- Experience implementing RAG architectures with vector databases
- Proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI)
- Experience integrating LLMs via APIs
- Knowledge of AI governance, model lifecycle management, and evaluation
- Experience implementing Model Context Protocol (MCP)
- Experience with AI guardrails, content filtering, and safety controls
- Strong understanding of data privacy and handling sensitive data (PII/PHI)
Preferred Skills:
- 2+ years of experience building multi-agent or autonomous workflows
- Experience optimizing LLM cost, token usage, and performance
- Familiarity with enterprise AI deployment patterns and scalability
- Experience working in regulated environments (healthcare/government)
Eligibility:
- Must be eligible to work in the U.S. without sponsorship.
- Must be able to work onsite in Austin, TX (3 days/week required)
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