Enterprise AI Engineer
Design and implement applied AI workflows, LLM integrations, and retrieval systems that create measurable operational value in enterprise environments.
Quix is looking for an Enterprise AI Engineer to bridge the gap between AI capability and real-world enterprise deployment. This role is for someone who understands both the technical mechanics of large language models and retrieval-augmented systems, and the operational, governance, and integration constraints that define what responsible AI implementation looks like in complex organizations.
What You’ll Do
- Design and implement AI-assisted workflows integrated with enterprise data sources, APIs, and operational systems.
- Build and evaluate retrieval-augmented generation pipelines that ground LLM outputs in structured enterprise data.
- Establish evaluation frameworks that measure output quality, reliability, and boundary behavior in production.
- Implement governance controls: access restriction, content filtering, audit logging, and output validation.
- Collaborate with data, integration, and engineering teams to ensure AI components have reliable data foundations.
- Document AI system behavior, limitations, and operational requirements for both technical and stakeholder audiences.
- Assess vendor AI APIs and open model alternatives against specific use case requirements and risk profiles.
What We’re Looking For
- Demonstrated experience building production AI or LLM-integrated applications beyond proof-of-concept.
- Strong understanding of retrieval architectures: vector databases, embedding pipelines, semantic search, and reranking.
- Proficiency with Python-based AI tooling: LangChain, LlamaIndex, or equivalent orchestration frameworks.
- Experience with prompt engineering, context window management, and structured output generation.
- Ability to evaluate AI system outputs rigorously and design test suites for non-deterministic behavior.
- Understanding of AI governance, risk assessment, and data privacy constraints in enterprise contexts.
Nice to Have
- Experience with fine-tuning workflows or supervised alignment techniques.
- Background in regulated environments requiring explainability, audit trails, or AI risk documentation.
- Experience integrating with enterprise data platforms: Snowflake, Databricks, Azure Synapse, or equivalent.
AI implementation that actually works in enterprise environments is rare and valuable. This role produces AI-assisted systems that clients can trust, audit, and operate — moving beyond demonstrations into reliable operational capability.
Submit for Enterprise AI Engineer
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