Artificial Intelligence Engineer

• Design, train and evaluate machine-learning models end to end (from data collection to prod deployment).
• Build robust data pipelines and model-serving APIs that scale to thousands of requests per second.
• Own experiments: define metrics, set up A/B tests, analyse results, iterate fast.
• Collaborate daily with product, design and backend teams to translate ML insights into user-facing features.
• Contribute to an engineering culture that values code quality, automated testing and clear documentation., * Builder-mindset: you rapidly turn ideas into working prototypes, gather feedback, and refine.
• Product-oriented: you see beyond the model-every metric maps to a business or user outcome.
• Collaborative communicator: you can explain complex ML concepts to non-engineers and incorporate their perspectives.
• Continuous learner: new papers, tools and methods excite you more than they intimidate you.

What We Offer

• Remote-first culture with optional co-working stipends.
• Fast growth path: your work will directly shape the company’s core technology and culture.
• Flexible PTO and working hours-results matter more than clock-watching.

We’re looking for a hands-on AI Engineer who loves shipping code as much as training models and who thrives in the fast-moving, build-measure-learn rhythm of a startup., Must-have skills

• Strong foundations in machine learning & deep learning (supervised, unsupervised and transfer learning).
• Proficiency in Python and modern ML frameworks such as PyTorch or TensorFlow.
• Experience building and operating data/ML pipelines (ETL, feature stores, data versioning).
• Solid software-engineering practices: Git workflows, CI/CD, unit & integration testing.
• Familiarity with containerisation (Docker), orchestration (Kubernetes or similar) and at least one major cloud provider (AWS, GCP or Azure).
• Proven ability to monitor, debug and optimise models in production (latency, cost, drift).
• Hands-on exposure to MLOps stacks (MLflow, Kubeflow, Vertex AI, SageMaker, etc.).
• Knowledge of privacy & security best practices (GDPR, SOC 2, secret management).
• Experience with graph-based approaches.
• Contributions to open-source ML projects or publications.