AI Engineer

Job Description

The Position

We are looking for an AI Engineer. Our team supports the business areas: research and development, manufacturing, supply chain, commercial and animal monitoring thought data science and AI delivery. This role allows you to work on impactful AI products and collaborate with AI engineers, data scientists, building a community that learns, challenges, and inspires each other. Our team is shaping the Data Science and AI strategy, serves as a DS and AI community facilitator and helps non-technical colleagues to mature their understanding of DS and AI.

What will you do?

Write efficient and maintainable code following best practices in software engineering.

Working with DevSecOps tools for deploying and versioning code.

Select appropriate retrieval techniques, language models, and generative AI methodologies.

Continuously iterate and refine retrieval augmented generation (RAG) methods and processes based on experimentation, analysis, and feedback.

Document AI models, algorithms, and processes thoroughly.

Prepare technical reports and presentations to communicate results and methodologies.

Stay up to date with the latest advancements in AI and machine learning technologies.

Participate in Agile ceremonies with the team to execute on prioritized projects and features.

Implement automated testing and monitoring techniques to ensure the accuracy and reliability of AI systems.

Qualifications, Skills & Experience Required

Bachelors or Masters in Artificial Intelligence, Machine Learning, or Computer Science is highly preferred, but qualifications in other quantitative disciplines are appreciated as well

2+ years of experience in AI Engineering

Strong communication and collaboration skills

Strong programming skills in Python

Must be analytical, detail-oriented, and able to balance multiple projects simultaneously

Familiarity with building scalable AI Solutions within a modern technology stack which includes cloud services, data pipelines, database, and other necessary tooling

Familiarity with CI/CD and test-driven development

Familiarity with building Restful APIs for AI models

Familiarity with of machine learning concepts (i.e. neural networks, optimization algorithms, evaluation metrics)

Familiarity with Retrieval Augmented Generation (RAG) techniques

Familiarity with prompt engineering techniques, such as instruction design, template-based approaches, rule-based conditioning, or fine-tuning strategies