AI/ML Engineer

118-WW TMG MFG OPS; Richardson (TX); On-site; USD 150,000 - 200,000; Full time

Job summary

Texas Instruments in Richardson, TX is seeking a Sr. AI/ML engineer to design and deploy scalable ML solutions for the manufacturing floor. You’ll leverage GenAI tools to accelerate development and collaborate with engineering and data teams to deliver production-grade ML applications.

You will build end-to-end pipelines, deploy models, monitor performance, and contribute to best practices in a fast-paced semiconductor environment.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Electrical 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; experience with at least one CV framework.
• Generative AI exposure — hands-on experience with LLMs, prompt engineering, or RAG systems; familiarity with API providers.
• Database & data pipeline knowledge — relational and NoSQL databases; experience building or optimizing data pipelines.
• Software engineering practices — Git/GitHub, JIRA, CI/CD concepts; writing clean, testable Python code.

Responsibilities

• Develop end-to-end ML pipelines — implement workflows with structured and unstructured data.
• Work with GenAI & LLMs — contribute to RAG systems, prompt engineering, and agentic workflows.
• Collaborate on data architecture — design and optimize data pipelines across database systems.
• Build and prototype solutions — develop POCs for manufacturing problems and productionize models.
• Deploy and monitor models — end-to-end deployment, monitoring, retraining, and troubleshooting.
• Write quality code — production-ready code with testing, documentation, Git workflows.

Job description

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. As a global organization, we strive to create solutions that are compatible with all TI sites. We’re looking for candidates to join our team in Richardson, TX as we leverage modern technologies to deliver data, analytics and AI solutions that enhance quality and productivity in semiconductor manufacturing.

About the job:

Texas Instruments is looking for a Sr. AI/ML engineer who is experienced with developing and deploying AI/ML solutions at scale. This role is critical to accelerating our digital transformation through rapid development of quality and test solutions using cutting‑edge development techniques. The position offers the opportunity to revolutionize how our Smart Manufacturing and Automation team builds software while establishing best practices and scaling development capabilities across the organization.

We’re seeking a Machine Learning Engineer with solid foundational expertise to develop and deploy intelligent solutions across our smart manufacturing platform. What sets this role apart: you’ll leverage cutting‑edge AI‑assisted development platforms — Claude Code, GitHub Copilot, Cursor, and emerging agentic framework‑to dramatically accelerate your development velocity while building production‑grade ML systems. You’ll collaborate with senior engineers and cross‑functional teams to translate manufacturing challenges into scalable ML applications that directly impact manufacturing quality and test operations. This role emphasizes hands‑on execution, rapid iteration, and learning: you’ll build features that matter while growing your expertise in both ML engineering and AI‑amplified development practices.

Key Responsibilities

• 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

Minimum Requirements

• 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

Preferred Qualifications

• 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