AI/ML Engineer

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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 multi-agent solution 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

• 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

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 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

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
• 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