AI/ML Engineer, Plain Concepts

• More than 5 years of experience in AI / Machine Learning / Computer Science
• Experience building an “end-to-end software product” which has AI or Machine Learning components
• Experience working with Azure or other cloud-based solution
• Strong skills in Python and reasonable SQL understanding
• Experience in testing as essential part of development (unit, integration, etc.)
• Knowledges in NLP and Computer Vision
• Hands-on experience across the end-to-end ML lifecycle, from development to production deployment, including designing and building scalable ML pipelines (MLOps experience is very valuable for the team)
• Experience using GitHub Copilot or similar AI coding tools in real development workflows
• Constant drive to learn and ability to learn quickly
• Strong team player mindset
• Fluent spanish (mandatory)

Desirable

• Titles? Meh, we’re not big on them, but let’s call this one Data Scientist like the other colleagues at the team
• As part of our international AI/ML squad, you’ll craft tailor-made solutions that wow our clients. We’re hunting for a passionate AI Engineer with a solid technical background. Your mission? Train, deploy, and put groundbreaking developments into action — and that’s just the beginning
• You’ll tackle the unique challenges of AI-powered software alongside a team of brilliant minds
• You’ll dive into cutting-edge projects using the latest tech to push boundaries and make an impact
• Whether you prefer working from home or vibing with us at our offices, the choice is yours
• AGILE isn’t just a buzzword here — it’s how we roll. Multidisciplinary teams? Check. Full ownership of projects? Double check
• Ready to take on projects that matter, with a team that’s as passionate as you are? Let’s make it happen!
• Participating in the design and development of AI solutions for challenging projects
• Building production level ML/AI solutions, with solid software engineering and ML/AI principles
• MLOps Automated deployment and monitoring (models and infrastructure)
• Data analysis (data cleaning, variable transformation, etc.)
• Developing and training ML/AI models
• Putting AI models into production. This means parallelizing, optimizing, tuning, testing the models to deploy in a production environment
• Flexible schedule 35 Hours / Week

• The selection process: Simple, just 3 steps
• Phone screen
• 2 interviews with the team 🤘