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oracle

AI Engineer

Worthington Enterprises
Columbus, USA
Full-time
Mid · 3–5 years experience
Onsite
Discovered 1 weeks ago
dockermlflowpythonsharepointsnowflakesql

Full Job Posting

Preferred Qualifications

  • Snowflake AI/ML experience, especially Cortex Agents, Cortex Search, semantic views, Snowpark, Model Registry, Feature Store, or related capabilities.
  • n8n or comparable workflow/agent orchestration experience such as Workato, LangGraph, Semantic Kernel, or similar tools.
  • MCP, function/tool calling, structured outputs, event-driven integrations, containers, serverless services, or cloud application delivery.
  • LLMOps/MLOps practices, evaluation frameworks, observability, model/prompt versioning, Docker, CI/CD, or MLflow.
  • Enterprise knowledge and productivity integrations involving Microsoft 365, SharePoint, Teams, or governed data catalogs such as Alation.
  • Experience applying AI in manufacturing, supply chain, finance, operations, or another industrial domain.
  • Team and Collaboration Context
  • You will join the Corporate IT – Data, Analytics & AI team and partner with data engineering, analytics, architecture, security, infrastructure, and business functions. We value curiosity, sound judgment, disciplined experimentation, and people who enjoy turning difficult business problems into simple, reliable experiences.
  • Why Worthington?
  • Worthington is applying modern AI inside a real operating business, where better data, smarter automation, and trusted decision support can improve how work gets done every day. This role offers the opportunity to build enterprise AI products that move beyond demos and become part of the way employees ask questions, automate tasks, and make decisions.

Benefits

  • Health, dental, and vision insurance starting on the first day, including employer HSA contribution.
  • On-site medical center, pharmacy, barbershop, and fitness center.
  • 401(k) match plus employer contribution.
  • Employee Assistance Program.
  • Flexible work schedule.
  • Quarterly profit sharing, with no cap.

Qualifications

  • Bachelor’s degree in computer science, data science, engineering, information systems, or a related field—or equivalent practical experience.
  • Typically 3–5 years of relevant experience in AI/ML engineering, software engineering, data engineering, or data science, including evidence of delivering at least one AI-enabled solution beyond a one-off prototype.
  • Strong Python and SQL skills, plus experience with APIs, source control, testing, and maintainable application or pipeline development.
  • Hands-on experience with one or more of the following: LLM applications, RAG, tool-using agents, natural-language analytics, ML pipelines, or intelligent automation.
  • Understanding of data modeling, identity and access control, privacy, secure development, and responsible AI principles.
  • Ability to evaluate AI system quality systematically and diagnose failure modes rather than relying on anecdotal testing.
  • Strong collaboration and communication skills, including the ability to explain tradeoffs to both technical and business partners.

Responsibilities

  • Design, build, deploy, and improve AI applications including enterprise agents, retrieval-augmented generation (RAG), natural-language analytics, predictive models, and workflow automations.
  • Build Snowflake-native AI and ML solutions using capabilities such as Cortex Agents, Cortex Search, semantic views and structured-data tools, Cortex AI functions, Snowpark, Model Registry, and Feature Store where appropriate.
  • Engineer retrieval and context pipelines for structured and unstructured data, including ingestion, chunking, metadata, hybrid search, citations, permissions, and freshness.
  • Create reliable agentic workflows in n8n or comparable orchestration platforms, incorporating tool calling, human approval, retries, idempotency, secrets management, auditability, and exception handling.
  • Develop secure integrations with Microsoft 365, SharePoint, Teams, enterprise APIs, databases, file sources, and Model Context Protocol (MCP) tools.
  • Establish evaluation and monitoring practices for answer quality, task completion, drift, safety, latency, cost, adoption, and business value.
  • Apply software engineering practices including modular design, automated testing, code review, Git-based delivery, CI/CD, documentation, and production support.
  • Partner with business teams to frame opportunities, prototype quickly, translate ambiguity into a delivery plan, and guide adoption of solutions that are useful and trusted.
  • Contribute reusable patterns, standards, and guardrails that help other teams and AI Champions build responsibly.
  • Continuously evaluate new models and capabilities, separating durable value from short-lived hype.
  • Bachelor’s degree in computer science, data science, engineering, information systems, or a related field—or equivalent practical experience.
  • Typically 3–5 years of relevant experience in AI/ML engineering, software engineering, data engineering, or data science, including evidence of delivering at least one AI-enabled solution beyond a one-off prototype.
  • Strong Python and SQL skills, plus experience with APIs, source control, testing, and maintainable application or pipeline development.
  • Hands-on experience with one or more of the following: LLM applications, RAG, tool-using agents, natural-language analytics, ML pipelines, or intelligent automation.
  • Understanding of data modeling, identity and access control, privacy, secure development, and responsible AI principles.
  • Ability to evaluate AI system quality systematically and diagnose failure modes rather than relying on anecdotal testing.
  • Strong collaboration and communication skills, including the ability to explain tradeoffs to both technical and business partners.

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