AI Engineer; Remote

Location: Remote

Virtual, work-from-home position. Work anywhere in the US, must live in the US

We are seeking an experienced AI Engineer who is motivated, creative, and personable to join our dynamic team. This role requires a strong combination of technical expertise—particularly in software and AI—and real-world experience in building and deploying AI-powered systems. The ideal candidate will be focused on ensuring that technical AI solutions solve real-world problems and deliver measurable business outcomes. The AI Engineer will be responsible for moving models out of a notebook environment and integrating them into a live, scalable, and monitored system.

The AI Engineer will collaborate closely with other engineers and the broader DAIA team to operationalize machine learning and generative AI models at enterprise scale. This role will also help define best practices and guidelines for engineering teams.

• AI & Automation Discovery: Partner with stakeholders across the organization to identify and prioritize high-value opportunities for AI and automation.
• Build & Ship: Design, develop, and support AI & automation systems that drive measurable business outcomes—from initial scoping to production deployment.
• Platform Engineering: Design, build, and maintain scalable infrastructure, including frontend interfaces, backend services, and the underlying AI platform layer.
• Data Pipelines & Model Integration: Create data pipelines and AI model context protocols, leveraging tools like MCP, Lang Chain, vector databases, and semantic search.
• Reliability & Security: Ensure the high availability, performance, and security of AI platform services.
• Extensibility: Contribute to platform growth by authoring SDKs, APIs, and comprehensive developer documentation.
• Communication & Alignment: Translate complex AI concepts for non-technical stakeholders and keep business goals front and center.
• Stay Sharp: Keep pace with rapidly evolving AI models, frameworks, and research—and bring that knowledge back to the team.

• Education: Bachelor’s degree in Computer Science, Data Science, or a related field—or equivalent demonstrated experience through portfolio work, open-source contributions, or professional practice.
• Experience: Minimum of 3–5 years of experience in Software Engineering, Data Engineering, or Data Science, with meaningful hands‑on experience building and deploying AI or LLM‑powered systems.
• Technical
  
  Skills:
  
  
  • Builder & Deployer: You don’t just prototype—you ship. You have proven experience moving AI models out of notebooks and into live, scalable, monitored production systems.
  • AI & ML Practitioner: You are fluent in the full generative AI stack—LLMs, embeddings, vector databases, semantic search—and know how to apply them to real business problems.
  • Pipeline Pro: You have strong SQL skills, experience with data warehousing, and you build clean, reliable pipelines that others can depend on.
  • Software Craftsperson: You write production-ready code. OOP, clean architecture, testable systems—these aren’t buzzwords to you, they’re defaults.
  • Automation Native: You’ve worked with automation tools (Zapier, Gumloop, n8n, or similar) and know when to reach for them versus when to build something custom.
  • Strategic & Systems Thinker: You connect technical decisions to business outcomes. You can hold the big picture and the implementation detail at the same time.
  • Communicator: You’re as comfortable in a stakeholder meeting as you are in a code review.
  • Ethics-First: You believe in responsible, transparent AI practices and bring that lens to everything you build.
  • Culture Fit: You thrive in a remote-first environment, are self-directed, and genuinely enjoy collaborating across functions. Curiosity is your default setting.
• Preferred but Not
  
  Required:
  
  
  • Full-Stack Fluency: You’ve built end-to-end systems with React/Type Script/Next.js frontends and Python/FastAPI or Node.js backends—and you’re comfortable managing data with Postgres or Redis.
  • Quantitative Thinker: You have a solid grounding in statistics, probability, and experimental design. You know how to evaluate model performance rigorously, design…