AI Engineer - DS&ML

Toyota Tsusho Systems Corporation; Plano (TX); Hybrid; USD 130,000 - 190,000; Part time

Job summary

Toyota Tsusho Systems US, Inc. is seeking an experienced AI Engineer to design and operationalize the DS/ML AI Accelerator foundation. You will develop generative AI systems with RAG, agent workflows, and secure multi-agent orchestration to deliver measurable value.

In this role, you will ensure governance, traceability, and responsible AI practices, partnering with data/SMEs and product teams. The position is hybrid with in-office work four days weekly and runs as a contract from Oct 2026 to

Qualifications

• Bachelor's degree or equivalent practical experience in CS/DS/ML or related field.
• 5+ years in software, data science, ML, or AI engineering with production-quality Python.
• Hands-on experience building LLM-enabled applications, RAG systems, or agentic workflows using APIs and structured tool integrations.
• Strong Python skills and modern software engineering practices: testing, code review, CI/CD, API design, documentation, observability.

Responsibilities

• Onboard approved documents and knowledge into a governed knowledge base with metadata and source citations.
• Design and implement retrieval pipelines, evaluation metrics, and ensure outputs distinguish facts, inferences, and needs for SME confirmation.
• Build secure multi-agent workflows coordinating LLMs and deterministic steps with tool contracts and clear stop conditions.
• Define governance, scoring, and human-review points to ensure reliable, traceable AI outputs.

Skills

Python development

LLM-enabled applications

RAG systems

APIs

CI/CD

Data retrieval/indexing

Communication of tradeoffs

Responsible AI practices

Education

Bachelor's degree in Computer Science/Data Science/ML or related field

Job description

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.

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)