AI/ML Engineer
texas instrumentsRichardson (TX)
About the role
Overview:
In this role you will architect and deploy scalable ML systems to advance TI’s smart manufacturing platform. You’ll leverage GenAI, LLMs, and multi-agent workflows to accelerate digital transformation across manufacturing and test operations. You will work with cross-functional teams to translate challenges into production-ready ML solutions and establish standards for reliability and governance. This is a hands-on, fast-paced role focused on impact, velocity, and best-practice-driven delivery. Your work will directly influence manufacturing quality and efficiency across TI sites. Compensation / Benefits comprehensive benefits extensive health coverage and wellness programs competitive compensation career development opportunities flexible work arrangements equity or bonus programs unknown
Responsibilities:
Design end-to-end ML pipelines across structured/unstructured data; utilize LLMs, RAG, agents and multi-agent systems Lead GenAI initiatives including LLM-based solutions, prompt engineering, and MCP integrations Bridge data infrastructure with reliable ML workflows across segregated DBs; optimize data pipelines for real-time and batch processing Prototype and scale POCs with cross-functional partners; plan productionization with reliability, monitoring, and governance Own full deployment lifecycle including DevOps, CI/CD, containerization, monitoring, retraining, and drift detection Mentor junior ML engineers; conduct code reviews; enforce reproducibility, testing, and documentation Participate in end-to-end model deployment; monitor performance and troubleshoot production issues Write production-quality code with testing and documentation; follow Git/JIRA workflows and agile practices
Key requirements:
Bachelor's degree in a related field 5+ years of applied ML engineering experience Hands-on experience with Generative AI & LLMs, prompt engineering, RAG systems, agents and multi-agent orchestration Production ML engineering experience including model versioning, monitoring, retraining, drift detection Strong software engineering fundamentals; proficient in Git/GitHub, JIRA; production-quality Python code Familiarity with relational and NoSQL databases; experience building data pipelines Experience with SDLC, agile development, CI/CD concepts collaboration mentorship agile mindset Generative AI & LLM engineering RAG systems agents and multi-agent orchestration
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