Advantest is seeking a highly experienced Senior Principal AI Systems Engineer to architect and deliver
advanced production AI capabilities. This role is for a hands-on technical leader with deep expertise in
agentic AI, open-source model development, model fine-tuning, evaluation systems, and reliable AI
infrastructure.
The ideal candidate combines advanced AI knowledge with strong software-engineering discipline and
has experience building systems that reason across multiple steps, use tools, evaluate results, recover from
failures, and improve from feedback.
Key Responsibilities
Agentic AI Systems
• Architect production-grade systems for tool-using and multi-step AI agents.
• Design orchestration, planning, memory, session management, retries, timeouts, observability, and failure
recovery.
• Establish structured and typed interfaces between models, tools, data services, and applications.
• Build controls that prevent agents from bypassing required validation and approval stages.
• Develop reusable agent frameworks that support multiple products and deployment environments.
Model Development and Fine-Tuning
• Fine-tune open-source language models and specialized models for complex technical applications.
• Apply LoRA, QLoRA, full fine-tuning, distillation, preference optimization, and related post-training
methods.
• Develop efficient strategies for adapting foundation models to new applications and datasets.
• Evaluate tradeoffs among model quality, inference performance, deployment cost, security, and
maintainability.
• Build optimized models and inference profiles for constrained deployment environments.
Evaluation and Quality
• Define measurable standards for model accuracy, reliability, safety, and production readiness.
• Build offline evaluation datasets, automated regression suites, judge systems, and promotion gates.
• Develop methods for measuring confidence, consistency, tool-use accuracy, and action quality.
• Establish processes for model comparison, controlled release, rollback, and continuous improvement.
• Ensure model outputs remain grounded in available evidence and approved data sources.
Training Data and Learning Pipelines
• Design reproducible pipelines for training-data generation, cleaning, labeling, versioning, and validation.
• Develop synthetic-data and preference-data strategies where appropriate.
• Implement controls for data leakage, contamination, duplication, provenance, and customer isolation.
• Convert expert feedback and observed outcomes into high-quality training and evaluation datasets.
• Maintain traceability between datasets, experiments, model versions, and production results.
AI Platform and MLOps
• Build repeatable training, evaluation, and deployment workflows for multi-GPU infrastructure.
• Establish model lifecycle practices, including model cards, release criteria, monitoring, and rollback.
• Support secure, private, air-gapped, and customer-controlled deployment environments.
• Develop observability for model behavior, tool execution, latency, cost, and failure conditions.
• Partner with platform and CI/CD teams to make AI workflows repeatable, testable, and auditable.
Technical Leadership
• Set technical direction for a small, highly skilled AI engineering team.
• Review architectures, models, training methods, and production implementation decisions.
• Mentor engineers and establish durable AI engineering practices.
• Work with domain experts to translate complex technical requirements into reliable AI capabilities.
• Communicate technical risks, tradeoffs, progress, and recommendations to engineering and executive
stakeholders.
• Extensive experience developing production AI or machine-learning systems.
• Deep expertise in agentic systems, tool-using models, multi-step reasoning, orchestration, and production
failure modes.
• Hands-on experience fine-tuning open-source language models.
• Experience with LoRA, QLoRA, full fine-tuning, distillation, or related model-adaptation methods.
• Experience with preference optimization, reinforcement learning, RLHF, RLAIF, DPO, or comparable post
training techniques.
• Demonstrated experience building closed-loop systems that propose actions, evaluate outcomes, and
improve from feedback.
• Strong Python and software-architecture skills.
• Experience with typed APIs, structured schemas, automated testing, and CI-compatible development
practices.
• Experience creating rigorous model evaluations, regression tests, and release criteria.
• Experience with GPU-based model training and optimized inference.
• Ability to operate at Senior Principal level, including architecture ownership, technical leadership, and
ambiguous problem solving.
• Strong written and verbal communication skills.
Preferred Qualifications
• Experience developing AI systems for complex engineering, scientific, industrial, or hardware-related
applications.
• Experience integrating AI models with external tools, APIs, simulators, instruments, or automation systems.
• Familiarity with retrieval-augmented generation and grounded AI systems.
• Experience with judge models, policy gates, approval workflows, or human-in-the-loop systems.
• Experience with secure or isolated model deployment.
• Familiarity with intellectual-property protection and customer-data isolation.
• Experience moving research-level AI methods into reliable commercial products.
• Background in semiconductor, electronic-design, manufacturing, test, or related technical industries is
beneficial but not required.
What Success Looks Like
• Establish a stable and reusable production AI architecture.
• Implement structured contracts, evaluation gates, observability, and behavioral regression testing.
• Deliver measurable improvements in model and agent performance.
• Define defensible model-promotion and release standards.
• Build repeatable model-training and adaptation workflows.
• Improve the reliability, security, and maintainability of the AI platform.
• Establish engineering practices that allow future applications to reuse the core AI foundation efficiently.
• Raise the technical standard for advanced AI development across the organization.
Ideal Candidate Profile
You are a deeply technical builder who understands both modern AI research and the realities of
production software. You are comfortable working across models, data, evaluation, infrastructure, and
application integration.
You do not rely on demonstrations or prompt engineering alone. You know how to create measurable,
reproducible, secure, and maintainable AI systems that perform reliably under real operating conditions