About TTS-US: 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. Position Summary: 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. Essential Functions:                    Description / Essential Functions: 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. Requirements Minimum qualification: Required Education & Experience: • Bachelor's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent practical experience. • 5+ years of software, data science, machine learning, or AI engineering experience, including production-quality Python development and collaborative version-controlled delivery. • Hands-on experience building LLM-enabled applications, RAG systems, agentic workflows, or comparable AI assistants using APIs and structured tool integrations. • Strong Python skills and experience with modern software engineering practices: testing, code review, CI/CD, API design, documentation, and observability. • Experience with data retrieval/indexing concepts, embeddings, vector or hybrid search, evaluation design, and quality measurement. • Ability to turn ambiguous business problems into testable technical requirements and communicate tradeoffs to technical and nontechnical partners. • Demonstrated commitment to responsible AI, privacy, security, and data-governance practices. Preferred Qualifications: • Experience with modern LLM orchestration, agent-workflow, or developer-assistance platforms. • Experience designing LLM evaluation frameworks, guardrails, prompt/version management, telemetry, or model-risk controls. • Experience supporting ML lifecycle workflows, feature or data documentation, model development, or ML project bootstrapping. • Familiarity with data governance, responsible AI, access control, auditability, and human-in-the-loop review. Position Type/Expected Hours of Work: • Hybrid work environment (4 days a week in office) • Contract position (Duration: October, 2026 – March 31st, 2027)