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AI Engineer – DS, ML
Toyota Tsusho Europe. Partner with data and business owners to onboard approved documents, data definitions, prior work, and expert knowledge into a governed knowledge base .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Python development, data retrieval, and machine learning applications, with a strong focus on responsible AI practices and data governance. Capable of designing and implementing workflows that integrate LLMs and ensure quality and security in outputs.
Highest-signal resume keywords
Python DevelopmentData Retrieval/IndexingLLM-Enabled ApplicationsResponsible AI PracticesAgile Methodologies
ATS Keywords
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Hard Skills
Software EngineeringMachine LearningAPI DesignTestingCode ReviewCI/CDObservabilityEvaluation DesignQuality MeasurementData Governance
Soft Skills
CommunicationProblem-SolvingCollaboration
Tools & Technologies
Version ControlAPIsAgentic WorkflowsTelemetryModel-Risk Controls
Industry Keywords
Data ScienceResponsible AIPrivacySecurityHuman-in-the-Loop Review
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- 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
- Create evaluation datasets and retrieval-quality metrics; diagnose relevance, grounding, completeness, and source-stewardship gaps
- Ensure generated outputs distinguish retrieved facts, inferences, assumptions, and items requiring subject-matter-expert confirmation
- Design and build secure multi-agent workflows coordinating LLM and deterministic steps, safely using tools, and producing structured, traceable outputs
- Define agent roles, tool contracts, workflow state, validation, error handling, stop conditions, and human review points
- Implement reusable prompt, workflow, and tool-orchestration patterns
- Define and instrument technical and user-centered evaluations for agent outputs
- 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
What you’ll need- 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
- 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 testing, code review, CI/CD, API design, documentation, and observability
- Experience with data retrieval/indexing, 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: experience with modern LLM orchestration, agent-workflow, or developer-assistance platforms
- Preferred: experience designing LLM evaluation frameworks, guardrails, prompt/version management, telemetry, or model-risk controls
- Preferred: experience supporting ML lifecycle workflows, feature or data documentation, model development, or ML project bootstrapping
- Preferred: familiarity with data governance, responsible AI, access control, auditability, and human-in-the-loop review