Applied AI & Data Engineer, Business & Education

What you'll do

• Design and build data and AI platforms using Databricks, AWS, and cloud-native architectures.
• Develop end-to-end agentic systems including retrieval, planning, evaluation, guardrails, and production deployment.
• Build infrastructure for GenAI features such as RAG pipelines, vector search, and knowledge graphs.
• Optimize system performance through model selection, latency management, and token economics at scale.
• Translate ambiguous stakeholder requirements into secure, scalable, and production-ready data systems.
• Provide technical leadership through design reviews, implementation guidance, and project lifecycle management.
• Mentor engineers and lead the adoption of AI-native practices through workshops and playbooks.
• Research and implement advanced techniques to automate data engineering workflows and provide natural-language data access.

What we're looking for

• 8+ years of experience in data, analytics, software, or ML engineering, including at least 3 years building and shipping production AI systems.
• Proficiency in designing and operating production AI products using LLMs, foundation models, agents, and deterministic components.
• Hands-on fluency with modern LLM/agent frameworks (LangChain, LlamaIndex), vector search, RAG pipelines, and multi-agent coordination.
• Experience building scalable data platforms on cloud-native systems like Databricks or AWS using Spark, Kafka, and Delta Lake.
• Proficiency in at least one high-level language (Python, Scala, Java, or Go) and strong SQL skills.
• 3+ years of experience mentoring engineers and providing technical leadership across concurrent initiatives.
• BS or MS in Computer Science, Information Systems, AI, Machine Learning, Engineering, Mathematics, Statistics, or a related field.
• Experience with MLOps/LLMOps, model fine-tuning, and building natural-language interfaces over data (preferred).