Back
Specialized LLM/vector skills limit applicants, but mid-level role and strong ERM brand increase competition.
Core AI/LLM engineering skills are transferable, though EHS domain preference raises industry-specific fit sensitivity.
Explicit 4–6 years plus mandatory LLM, LangChain, vector DB and Python requirements make filters stringent.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and implement AI-driven data pipelines transforming complex data into structured, business-ready outputs for clients.
Architect and orchestrate intelligent AI workflows combining LLMs, automation, and modern data engineering to enable production-ready AI capabilities.
Develop customer-facing AI solutions integrating back-end AI services with modern web interfaces and optimize AI workflows using relational and vector databases.
Bachelor’s degree in Environmental Sciences, Information Technology, Computer Science, Engineering, Management Information Systems, or related technical field.
4-6 years of relevant experience in AI data engineering and/or EHS-related field.
Hands-on experience with large language models (e.g., OpenAI, Anthropic, Mistral) and expertise in embeddings, vector search, semantic similarity, and RAG architectures.
Strong skills in Python (including async, pandas/polars, AI SDKs), SQL, API development (FastAPI, .NET, Node.js), and programmatic generation of Excel, PDF, and Word documents.
Proven ability to bridge AI research and engineering to build scalable, enterprise-grade AI-driven data solutions with measurable business impact.
Experienced in full-stack AI application development including integrating AI back-end services with front-end technologies (Vue 3, TypeScript preferred).
Comfortable working with vector databases, containerization, cloud/serverless architectures, and implementing best practices for code quality and maintainability in large-scale projects.