AI Engineer
New
Estonia; Fully remote, Europe.Full-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 2+ years
- Required Skills
- Node.jsPythonMachine LearningSoftware ArchitectureTypeScriptData scienceRESTful APIsPrompt Engineering
Requirements
- 2+ years of professional data science, applied AI or machine-learning engineering experience.
- Experience building AI-enabled features, prototypes or production applications using LLM APIs or related AI services.
- Strong programming ability in Python, TypeScript/Node.js or similar languages.
- Experience with prompt design, structured outputs, retrieval workflows, AI orchestration or agentic workflows.
- Experience evaluating AI quality, reliability and safety beyond informal manual testing.
- Solid understanding of APIs, databases, cloud services and production software architecture.
- Ability to balance experimentation with disciplined engineering, testing, privacy and security standards.
- A self-directed approach to investigating new methods, testing assumptions and improving product quality.
- Comfortable working with non-technical stakeholders and education partners to understand the real problem behind a feature request.
- Strong ownership, debugging and problem-solving skills.
- Clear written and spoken English.
Responsibilities
- Design and build AI-enabled features for various education products, from assessments to tutoring.
- Work with education partners and internal stakeholders to understand assessment practices, quality expectations and real-world constraints.
- Build evaluation approaches that measure output quality, consistency, usefulness and failure modes.
- Develop retrieval, prompt-management, orchestration and structured-output workflows where appropriate.
- Integrate LLM APIs, AI services and education data into reliable product capabilities.
- Design practical human-review, control and escalation workflows for AI-supported decisions.
- Improve the reliability, latency, cost and quality of AI services in production.
- Build prototypes quickly, test them against real education use cases and productionise successful approaches.
- Develop suitable guardrails, monitoring and auditability for AI outputs.
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