AI Engineer - DS&ML

Toyota Tsusho Systems US, Inc.; Plano (TX); Hybrid; USD 140,000 - 190,000; Part time

Benefits offered by this job

Hybrid work environment (4 days in the

Contract position (Oct 2026–Mar 31, –

Job summary

Toyota Tsusho Systems US, Inc. in Plano, TX seeks an experienced AI Engineer to design, build, evaluate, and operationalize the technical foundation of the DS/ML AI Accelerator, with emphasis on governance, traceability, and measurable delivery impact.

You will lead hands-on generative AI engineering, RAG pipelines, and agent workflow design. Responsibilities include building secure multi-agent workflows, defining tool contracts and validation, and ensuring outputs clearly distinguish retrieved

Qualifications

• 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 with production-quality Python
• Hands-on experience building LLM-enabled applications, RAG systems, agentic workflows, or comparable AI assistants
• Strong Python skills and experience with testing, CI/CD, API design, documentation, and observability
• Experience with data retrieval/indexing concepts, embeddings, vector/hybrid search, evaluation design, and quality measurement
• Ability to translate ambiguous business problems into testable technical requirements and communicate tradeoffs
• Commitment to responsible AI, privacy, security, and data-governance practices

Responsibilities

• Governed Knowledge and RAG: onboard documents, data definitions, and expert knowledge into a governed knowledge base
• Implement retrieval pipelines, metadata tagging, source citations, and quality checks
• Create evaluation datasets and retrieval-quality metrics; diagnose gaps
• Agentic Product Engineering: design secure multi-agent workflows, tool contracts, and human review points

Skills

Python programming

LLM / AI systems

CI/CD

Version control (Git)

Education

Bachelor's degree in Computer Science or related field

Tools

CI/CD pipelines

APIs

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

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)