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Senior Applied AI Engineer
Not specifiedFull-timeAI EngineeringElectronics Manufacturing
Develop and operate production AI solutions that unify engineering data, enable advanced analytics, and improve productivity across ASIC networking product engineering workflows.
Responsibilities
- Design, build, and maintain AI solutions for production, characterization, analysis, and operational workflows.
- Develop AI agents and copilots that allow engineers to query, analyze, and reason over ASIC data.
- Consolidate data from infrastructure and engineering systems into scalable pipelines and reusable services.
- Partner with production engineering teams to identify use cases and deliver measurable improvements.
- Build tools for data access, automation, reporting, anomaly detection, and engineering insight generation.
- Collaborate across teams to improve interfaces and data quality and support scalable deployment.
- Use feedback, monitoring, and roadmap planning to drive continuous improvement.
Requirements
- Bachelor’s degree in computer science, software engineering, data science, or a related field, or equivalent experience.
- At least 8 years of experience as an AI solutions engineer, machine learning engineer, or software engineer building production AI or data solutions.
- Strong experience designing, developing, deploying, and maintaining end-to-end production AI applications.
- Hands-on expertise with Python and modern software engineering practices.
- Practical experience with large language models, AI agents, retrieval-augmented generation, workflow orchestration, and data analytics applications.
- Strong experience building data pipelines, APIs, services, and applications using structured and semi-structured engineering data.
Nice to have
- Experience supporting AI solutions for engineering or manufacturing organizations.
- Familiarity with agent frameworks, vector databases, telemetry platforms, or internal knowledge and data systems.
- Experience spanning software, data, infrastructure, and product engineering.
- Experience introducing technical capabilities and driving adoption across engineering teams.
- Background in semiconductor, hardware, product engineering, testing, characterization, or manufacturing analytics.