AI Solutions Engineer

AI Solutions Engineer

Location:

Ecuador

Job type:
Full time, Contractor – Remote

At QS, we believe that work should empower you. That's why we foster a flexible working environment that encourages every employee to own their career whilst flourishing personally and professionally. Our company values underpin everything we do – we collaborate, respect and support each other.

It's our mission to empower motivated people around the world to fulfil their potential through higher education, ensuring that everyone has access to opportunities that change lives.

Our diversity makes us stronger. By sharing our experiences, we learn from one another and achieve more together, driving progress across the sector.

At QS, you'll be responsible for implementing real change in the international higher education landscape. You'll take on meaningful challenges that see a positive impact across the business and the wider sector.

We're confident you'll feel right at home here. QS was named as one of Newsweek's Top 100 Most Loved Workplaces® in the UK (October 2023), recognising the respect, trust and appreciation that drive our culture every day. And as a gold-accredited Investors in People organisation – putting us among the top 28% of workplaces globally – it's official: QS is a place where everyone can thrive.

As a AI Solutions Engineer, this is what you'll be doing:

We are seeking an AI Solutions Engineer to join our team and help design, build, and deploy AI-powered products and solutions that deliver meaningful business value.

As an AI Solutions Engineer, you will work at the intersection of engineering, data, and artificial intelligence. You will collaborate closely with Product, Data, Engineering, and Machine Learning teams to identify opportunities where AI can improve user experiences, automate workflows, and unlock new capabilities across our platforms.

In this role, you will design and develop AI-powered applications, agentic workflows, and intelligent automations using Large Language Models (LLMs), retrieval systems, and modern AI frameworks. You will be responsible for transforming business requirements into scalable AI solutions, integrating AI capabilities with existing data platforms and services, and ensuring solutions are reliable, secure, and cost-effective.

You will also contribute to the development of data pipelines, retrieval systems, evaluation frameworks, and deployment processes that support production AI systems. While this is not a research-focused role, a strong understanding of machine learning concepts, AI system design, and MLOps practices will be valuable.

We are looking for someone who is passionate about AI, enjoys solving complex problems, and is excited about applying emerging technologies to real-world challenges. This role offers the opportunity to shape the future of AI driven products that impact millions of students, learners, and professionals worldwide.

• Design, build, and deploy AI-powered applications, workflows, and automations using Large Language Models (LLMs) and modern AI frameworks.
• Develop AI agents capable of performing multi-step tasks, interacting with external systems, and supporting business processes.
• Design and implement Retrieval-Augmented Generation (RAG) solutions using structured and unstructured data sources.
• Collaborate with Product, Engineering, and Data teams to identify opportunities where AI can improve products, workflows, and user experiences.
• Support the build and maintenance of data pipelines that support AI applications and analytical workloads.
• Integrate AI services with internal platforms, APIs, databases, and third-party systems.
• Develop evaluation frameworks and testing methodologies to measure AI performance, reliability, and business impact.
• Work with structured and semi-structured datasets stored in cloud data warehouses and data lakes.
• Optimize prompts, workflows, retrieval strategies, and model configurations to improve accuracy, latency, and cost efficiency.
• Monitor, troubleshoot, and continuously improve production AI systems.
• Contribute to AI governance, security, and responsible AI practices.
• Document architectures, workflows,…