AI/ML Engineer

United States·Richardsonmid

Machine Learning EngineerData

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Quick Summary

Overview

Change the world. Love your job. The Smart Manufacturing and Automation team at Texas Instruments develops analytics solutions to address challenges faced by manufacturing and engineering teams.

Technical Tools

Machine Learning EngineerData

About the Role

~1 min read

Develop ML pipelines — implement end-to-end ML workflows combining structured and unstructured data; build data modeling for MFG data correlation inline and end of line data, feature engineering, training, and inference pipelines under technical guidance
Work with GenAI & LLMs — contribute to LLM-based features including RAG systems, prompt engineering, and agentic workflows; experiment with different approaches and document learnings
Collaborate on data architecture — work with data engineers to design and optimize data pipelines across our database systems; understand trade-offs between different storage architectures; integrate structured and unstructured data sources
Build and prototype solutions — develop POCs for manufacturing problems; translate requirements into ML experiments; contribute to productionization of models with appropriate monitoring and testing
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 AI/ML experience — proven experience developing and deploying ML solutions with measurable impact
Computer Vision basics — working knowledge of image processing and computer vision techniques (classification, detection, or anomaly detection); experience with at least one CV framework
Generative AI exposure — hands-on experience with LLMs, prompt engineering, or RAG systems; familiarity with OpenAI, Anthropic, or similar APIs; curiosity about multi-agent systems
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
Manufacturing or industrial domain exposure — familiarity with manufacturing environments, IoT systems, or operational processes; any exposure to semiconductor or discrete manufacturing
Production ML experience — experience deploying models to production; understanding of model versioning, monitoring, or retraining pipelines
DevOps & containerization — hands-on experience with Docker, Kubernetes, or cloud deployment (AWS, GCP, Azure); basic CI/CD pipeline knowledge
Multi-agent or advanced GenAI projects — exposure to agent frameworks, LangChain, or similar tools; experience with more complex LLM workflows beyond single-turn prompts