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Mid-level AI role in metro with broad LLM and data engineering requirements increases competition.
AI/LLM engineering skills are transferable, though EHS consultancy context adds some domain specificity.
Explicit 4–6 years plus mandatory LLM, LangChain, vector DB, and API skills imply high strictness.
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Design and implement AI-powered data pipelines to transform structured and unstructured data into business-ready outputs.
Orchestrate large language models (LLMs) and automation to create scalable AI workflows and customer-facing AI solutions.
Develop and integrate full-stack applications and optimize relational and vector databases to support AI workflows at enterprise scale.
University degree in Environmental Sciences, Information Technology, Computer Science, Engineering, Management Information Systems, or Theoretical Business.
4-6 years of relevant experience in AI data engineering and/or EHS-related field.
Hands-on experience with large language models (OpenAI, Anthropic, Mistral, or open-source equivalents) and strong Python proficiency including async patterns and AI SDKs.
Proficiency in SQL, data modelling, AI orchestration frameworks (e.g., LangChain, LlamaIndex) and ability to programmatically generate Excel, PDF, Word documents.
Experienced in bridging AI research with robust engineering to deliver enterprise-scale AI-driven business solutions.
Comfortable working on diverse technology stacks including Python, .NET, Node.js, and web front-end frameworks such as Vue 3 and TypeScript.
Familiar with advanced AI workflows involving embeddings, vector search, RAG architectures, and cloud/containerized environments.