T
AI Engineer - DS&ML
Toyota Tsusho Systems
2 hours ago
Full-time
On-site
AI Engineer Position
Founded in 2011, Toyota Tsusho Systems US, Inc. (TTS-US) is a Toyota group company that develops IT solutions wherever global businesses operate. Transforming into a technology and mobility company, TTS-US with its 8 TTS affiliates worldwide is establishing a secure and resilient Toyota global value chain. The creative capacity to forge such limitless business opportunities is one of the strengths of Toyota Tsusho Systems. We are seeking an experienced AI Engineer to design, build, evaluate, and operationalize the technical foundation of the DS/ML AI Accelerator. The successful candidate will be responsible for hands-on generative AI engineering, retrieval-augmented generation (RAG), agent workflow design, and software engineering with a strong focus on governance, traceability, and measurable delivery impact. Governed Knowledge and RAG
Partner with data and business owners to onboard approved documents, data definitions, prior work, and expert knowledge into a governed knowledge base. Implement retrieval pipelines, metadata, taxonomy, tagging, source citations, and quality checks that make context discoverable and trustworthy. Create evaluation datasets and retrieval-quality metrics; diagnose relevance, grounding, completeness, and source-stewardship gaps. Ensure generated outputs clearly distinguish retrieved facts, inferences, assumptions, and items that need subject-matter-expert confirmation. Agentic Product Engineering
Design and build secure multi-agent workflows that retrieve approved context, coordinate LLM and deterministic steps, use tools safely, and produce structured, traceable outputs. Define explicit agent roles, tool contracts, workflow state, validation, error handling, stop conditions, and human review points for reliable multi-agent operation. Implement reusable prompt, workflow, and tool-orchestration patterns that improve repeatability, traceability, and usability for Data Scientists, ML Engineers, Business Analysts, Product Owners, and SMEs. Evaluation, Delivery, and Governance
Define and instrument technical and user-centered evaluation for agent outputs, including correctness, completeness, traceability, revision effort, latency, and cost-to-serve. Work in two-week agile sprints; demonstrate working increments, document technical decisions and convert pilot feedback into a prioritized backlog. Apply secure development practices and meet Responsible AI, security, privacy, data-governance, and enterprise review-board requirements. Partner with the future operating owner to define maintainable code, runbooks, monitoring, knowledge-source refresh practices, and an enhancement backlog.
Founded in 2011, Toyota Tsusho Systems US, Inc. (TTS-US) is a Toyota group company that develops IT solutions wherever global businesses operate. Transforming into a technology and mobility company, TTS-US with its 8 TTS affiliates worldwide is establishing a secure and resilient Toyota global value chain. The creative capacity to forge such limitless business opportunities is one of the strengths of Toyota Tsusho Systems. We are seeking an experienced AI Engineer to design, build, evaluate, and operationalize the technical foundation of the DS/ML AI Accelerator. The successful candidate will be responsible for hands-on generative AI engineering, retrieval-augmented generation (RAG), agent workflow design, and software engineering with a strong focus on governance, traceability, and measurable delivery impact. Governed Knowledge and RAG
Partner with data and business owners to onboard approved documents, data definitions, prior work, and expert knowledge into a governed knowledge base. Implement retrieval pipelines, metadata, taxonomy, tagging, source citations, and quality checks that make context discoverable and trustworthy. Create evaluation datasets and retrieval-quality metrics; diagnose relevance, grounding, completeness, and source-stewardship gaps. Ensure generated outputs clearly distinguish retrieved facts, inferences, assumptions, and items that need subject-matter-expert confirmation. Agentic Product Engineering
Design and build secure multi-agent workflows that retrieve approved context, coordinate LLM and deterministic steps, use tools safely, and produce structured, traceable outputs. Define explicit agent roles, tool contracts, workflow state, validation, error handling, stop conditions, and human review points for reliable multi-agent operation. Implement reusable prompt, workflow, and tool-orchestration patterns that improve repeatability, traceability, and usability for Data Scientists, ML Engineers, Business Analysts, Product Owners, and SMEs. Evaluation, Delivery, and Governance
Define and instrument technical and user-centered evaluation for agent outputs, including correctness, completeness, traceability, revision effort, latency, and cost-to-serve. Work in two-week agile sprints; demonstrate working increments, document technical decisions and convert pilot feedback into a prioritized backlog. Apply secure development practices and meet Responsible AI, security, privacy, data-governance, and enterprise review-board requirements. Partner with the future operating owner to define maintainable code, runbooks, monitoring, knowledge-source refresh practices, and an enhancement backlog.