AI Data Solution Engineer

How to Apply: Interested and qualified? Go to Environmental Resources Management (ERM) on erm.wd3.myworkdayjobs.com to apply.

Show AI Engineering Depth Beyond the Buzzwords

This role sits at the intersection of AI, data engineering, and environmental consulting. Hiring managers will look for proof you can build production-grade pipelines, not just run experiments. Your CV and interview answers need to demonstrate hands-on experience with LLMs, vector databases, and modern front-end frameworks.

1. Tailor your CV to AI data engineering: Highlight projects where you built end-to-end AI pipelines, from data ingestion to customer-ready outputs. Use specific metrics like throughput, accuracy, or time saved. For example, mention a pipeline that processed 10,000 documents daily with 95% accuracy.

2. Master the technical stack: Be ready to discuss Vue 3 Composition API, TypeScript, vector databases (e.g., Pinecone, Weaviate), and containerization (Docker, Kubernetes). Prepare a short demo or code sample that shows your proficiency in these tools.

3. Understand the EHS domain: Since ERM focuses on environmental, health, and safety, familiarize yourself with common EHS data types (e.g., incident reports, compliance data). Show how your AI solutions can address real environmental challenges.

4. Prepare for system design questions: Expect to design an AI-powered data pipeline on a whiteboard or in a live coding session. Practice architecting systems that handle unstructured data, use LLMs for enrichment, and store results in vector databases.

5. Showcase your full-stack skills: This role requires integrating AI back-ends with modern web interfaces. Highlight any projects where you built a complete application, from database to UI, and mention the tech stack used.

6. Emphasize scalability and production readiness: Talk about how you've taken AI prototypes to production, including monitoring, error handling, and performance optimization. Employers want engineers who can make AI reliable at scale.

7. Communicate impact clearly: In interviews, articulate how your work influenced business decisions or client outcomes. Use the STAR method (Situation, Task, Action, Result) to structure your stories, focusing on measurable results.

8. Ask smart questions: Show genuine interest by asking about ERM's current AI initiatives, the team's tech stack, and how they measure success for AI projects. This demonstrates strategic thinking and alignment with their goals.