AI/ML Engineer

Onset Technologies•Austin, TX

3d•Hybrid

About The Position

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.

Requirements

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)
Must be eligible to work in the U.S. without sponsorship.
Must be able to work onsite in Austin, TX (3 days/week required)

Nice To Haves

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)

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