Analyst, AI Engineer

Build and support AI-ready access to WAI catalog, customer, order, product, sales-channel, and related business data as directed by AI platform leadership.

Assist with Infor integration and other trusted data connections so AI outputs are grounded in approved business data sources.

Implement semantic, keyword, and structured search across catalog, OE number, fitment, cross-reference, technical specification, and product data.

Parse and structure PDFs, Excel files, fitment guides, manufacturer specifications, RMA forms, customer questions, chatbot conversations, and other technical or business documents.

Build and support retrieval-augmented generation (RAG), embeddings, vector search, document ingestion pipelines, prompt workflows, and AI assistant capabilities for internal users and distributor support.

Capture and organize real user questions, failed searches, corrections, successful answers, and feedback to support evaluation data, retrieval improvement, and future model tuning.

Assist with evaluating, deploying, and testing approved hosted or open-source models such as Llama, Mistral, Qwen, or similar models under technical direction.

Support AI workflows related to Amazon Business trends, listing performance, returns, customer demand, and other approved business intelligence use cases as assigned.

Document technical logic, data mappings, prompts, retrieval sources, test cases, known issues, and support procedures.

Test AI outputs for source grounding, accuracy, consistency, reliability, and alignment with acceptance criteria.

Monitor assigned workflows, troubleshoot defects, analyze failures, and recommend improvements to retrieval quality, data quality, model behavior, and workflow performance.

Collaborate with offshore and onsite team members, IT, data owners, and business-facing AI resources to deliver approved work within WAI standards.

Undertakes additional responsibilities and tasks as directed by management.

What We're Looking For Education: Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, Artificial Intelligence, or a related technical field preferred; equivalent experience may be considered based on local market practice.

2+ years of experience in software development, data engineering, AI/ML, automation, analytics engineering, business systems, workflow tools, or related technical work.

Hands-on experience with Python, SQL, APIs, data pipelines, scripts, automation, or data workflows.

Hands-on exposure to or experience with large language models, prompts, RAG, embeddings, vector search, chatbots, AI agents, or document processing required; deeper or production-level experience across these areas strongly preferred.

working with ERP, catalog, product, customer, order, inventory, or technical-document data preferred.

supporting offshore delivery, remote collaboration, documentation, testing, or production support preferred.

with automotive aftermarket, manufacturing, distribution, product data, fitment data, or similar technical domains is a plus.

Competencies: Core Competencies & Leadership Attributes Analytical thinking and structured problem-solving.

Strong attention to detail, documentation discipline, and follow-through.

Curiosity and continuous learning in a fast-changing AI and data environment.

Ability to work from defined requirements, acceptance criteria, and technical direction.

Good judgment regarding data handling, source grounding, testing, escalation, and human review.

Collaborative mindset with onsite/offshore team members, IT, and business stakeholders.

Ability to communicate technical issues, blockers, and recommendations clearly.

Familiarity with Python, SQL, APIs, JSON, scripting, Git or version control, data workflows, and automation tools.

Working knowledge of AI-enabled tools, large language models, prompts, RAG, embeddings, vector databases, and document processing concepts.

Exposure to AI frameworks or tools such as OpenAI APIs, Azure AI, LangChain, LangGraph, Llama, Mistral, Qwen, or similar technologies preferred.

Ability to parse, clean, validate, and transform structured and unstructured data from business systems, documents, spreadsheets, and technical files.

Ability to test, validate, troubleshoot, and document AI workflows, retrieval outputs, prompts, and data pipelines.

Working knowledge of role-based access, auditability, source traceability, data quality, and responsible AI practices preferred.

Proficiency with Microsoft Office Suite, collaboration tools, and technical documentation practices.

English fluency required; additional languages are a plus.

Licensing or other special certifications: No required certifications.

Certifications in data analytics, cloud platforms, AI/ML, Python, SQL, Microsoft Azure AI, automation platforms, or related technical areas are preferred but not required.

TRAVEL REQUIREMENT: Limited travel is expected, estimated at 0-10%, based on business needs, project rollout requirements, training, workshops, or meetings.

Travel expectations are subject to local engagement model and business approval.