AI Engineer

The Opportunity/Role Description

As an AI Engineer in the V93000 documentation team, you will be a key contributor to building and enhancing our AI systems. You will help develop and advance our critical AI agent applications. Your work will focus on the full lifecycle of AI applications, from implementing, optimizing, and rigorously evaluating systems to meet performance benchmarks. This is a unique opportunity to prototype innovative solutions, bring them to production, and directly impact the future of Advantest's SOC test systems.

Role Responsibilities

• Design, implement, test, and continuously optimize end-to-end RAG pipelines, including data parsing, ingestion, prompt engineering, and chunking strategies.
• Curate and develop high-quality datasets, using synthetic data generation for robust training and evaluation.
• Rigorously evaluate LLM applications on metrics including correctness, latency, and hallucination.
• Assist in the deployment of LLM-based applications, analyze user feedback, and contribute to iterative improvements.
• Write clean, maintainable, and testable code following best practices.
• Collaborate with cross-functional teams to integrate AI components into other systems.

Qualifications

• Master’s or Ph.D. in Computer Science, Machine Learning, or a related field, and a minimum of 2 years of hands-on industry experience in software engineering.
• Experience operating RAG systems in production environments, including monitoring, debugging, and continuous improvement based on real user behavior.
• Solid understanding of software engineering practices applied to AI systems (testing, CI/CD integration, versioning, and reproducibility).
• Ability to balance research innovation with long‑term maintainability and customer‑ready quality standards.
• Clear communication and presentation skills.

Good To Have

• Experience with observability stacks (e.g., Prometheus, Grafana, OpenTelemetry) applied to AI or backend services.
• Familiarity with enterprise deployment constraints such as air‑gapped systems, license compliance, and distribution of AI‑enabled software to customers.
• Exposure to agent frameworks, tool‑calling patterns, or multi‑step reasoning architectures.
• Hands‑on experience with vector databases (e.g., Milvus) and modern RAG architectures, such as Graph‑based Retrieval‑Augmented Generation.

This role emphasizes long‑term ownership of Retrieval‑Augmented Generation systems as a core product capability, not just experimentation with large language models.