AI/ML Engineer

United States·Richardsonmid

Machine Learning EngineerData

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Quick Summary

Requirements Summary

Bachelor's degree in Computer Science, Software Engineering, Computer Graphics Technology, Electrical Engineering,

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Machine Learning EngineerData

About the Role

~2 min read

Architect ML systems at scale — design and implement end-to-end ML pipelines combining structured and unstructured data; leverage LLMs, RAG, agents, and multi-agent systems to solve manufacturing problems; own data ingestion, feature engineering, model training, and deployment workflows
Lead GenAI innovation — architect LLM-based solutions including RAG systems, agentic workflows, and MCP integrations; experiment with prompt engineering and multi-step reasoning to extract actionable insights from manufacturing data
Bridge data infrastructure — design ML workflows that work reliably across our segregated database systems; understand trade-offs between different DB architectures and optimize data pipelines for both real-time and batch processing
Build and scale POCs — rapidly prototype solutions with cross-functional partners; translate business challenges into ML experiments; establish clear paths to productionize high-impact POCs with reliability, monitoring, and governance
Deploy and maintain systems — own full lifecycle deployment including DevOps practices (CI/CD, containerization, infrastructure); establish monitoring, retraining pipelines, and drift detection; ensure models remain reliable in production
Lead the team — mentor junior ML engineers on best practices in ML engineering; conduct technical code reviews using Git/JIRA workflows; establish standards for reproducibility, testing, and documentation; drive agile sprint planning and technical discussions
Deploy and monitor models — participate in end-to-end deployment; own model monitoring, retraining pipelines, and performance tracking; troubleshoot production issues with senior engineers
Write quality code — develop production-ready code with testing and documentation; follow Git workflows, JIRA tracking, and agile practices; participate in code reviews and learn from feedback

Requirements

~2 min read

Bachelor's degree in Computer Science, Software Engineering, Computer Graphics Technology, Electrical Engineering, Computer Engineering or related field of study
5+ years of applied ML engineering experience — strong foundation in applied machine learning with proven impact in production systems
Generative AI & LLM engineering — hands-on experience building with LLMs, prompt engineering, RAG systems, agents, and multi-agent orchestration; familiar with MCP (Model Context Protocol) patterns
Production ML engineering — proven ability to build, deploy, and maintain ML systems in production; experience with model versioning, monitoring, retraining, and drift detection
Software engineering fundamentals — strong Git/GitHub workflows, JIRA/issue tracking, SDLC best practices, agile development; ability to write production-quality code with testing and documentation
Database & data pipeline knowledge — working familiarity with relational and NoSQL databases; experience building or optimizing data pipelines; understanding of structured vs. unstructured data handling
Software engineering practices — proficiency with Git/GitHub, JIRA, issue tracking; ability to write clean, testable Python code; familiarity with agile development and CI/CD concepts

Smart Manufacturing— prior work with IoT, manufacturing execution systems (MES), or connected factory environments
Advanced AI/ML capabilities — experience building with LLMs or generative AI APIs; familiar with prompt engineering and AI workflow optimization
Semiconductor manufacturing expertise — familiarity with semiconductor fab operations, yield optimization, defect detection, process control, or equipment diagnostics
Advanced multi-agent systems — experience designing and optimizing multi-agent LLM systems; familiar with agent communication patterns and task decomposition
Leadership & mentorship track record — proven ability to grow junior engineers and establish technical standards; experience in cross-functional team environments
Real-time ML systems — experience with streaming data, online learning, or low-latency inference for operational systems
MLOps & governance — expertise in feature stores, experiment tracking, model governance, or regulatory compliance in manufacturing