AI Engineer
New
C
ContinuedEdTech
US, core U.S. business hours in your time zoneFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3-5 years
- Required Skills
- Node.jsPythonSQLArtificial IntelligenceTypeScriptData engineeringData scienceFastAPIPostgresRedisNext.jsReactSoftware EngineeringLLMGenerative AILangChain
Requirements
- Bachelor’s degree in Computer Science, Data Science, or a related field (or equivalent demonstrated experience).
- Minimum of 3–5 years of experience in Software Engineering, Data Engineering, or Data Science.
- Meaningful hands-on experience building and deploying AI or LLM-powered systems.
- Proven experience moving AI models out of notebooks and into live, scalable, monitored production systems.
- Fluent in the full generative AI stack (LLMs, embeddings, vector databases, semantic search).
- Strong SQL skills and experience with data warehousing.
- Ability to build clean, reliable data pipelines.
- Writes production-ready code with OOP, clean architecture, and testable systems.
- Experience with automation tools (Zapier, Gumloop, n8n, or similar).
- Strategic and systems thinker, connecting technical decisions to business outcomes.
- Comfortable communicating with technical and non-technical stakeholders.
- Believes in responsible, transparent AI practices.
- Thrives in a remote-first, self-directed, collaborative environment.
- Preferred: Full-Stack Fluency (React/TypeScript/Next.js frontends, Python/FastAPI or Node.js backends, Postgres or Redis).
- Preferred: Solid grounding in statistics, probability, and experimental design for model performance evaluation.
Responsibilities
- Partner with stakeholders to identify and prioritize high-value opportunities for AI and automation.
- Design, develop, and support AI & automation systems that drive measurable business outcomes—from initial scoping to production deployment.
- Design, build, and maintain scalable infrastructure, including frontend interfaces, backend services, and the underlying AI platform layer.
- Create data pipelines and AI model context protocols, leveraging tools like MCP, LangChain, vector databases, and semantic search.
- Ensure the high availability, performance, and security of AI platform services.
- Contribute to platform growth by authoring SDKs, APIs, and comprehensive developer documentation.
- Translate complex AI concepts for non-technical stakeholders and keep business goals front and center.
- Keep pace with rapidly evolving AI models, frameworks, and research—and bring that knowledge back to the team.
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