> Markdown version of [/jobs/ext/3875093-senior-ai-developer](https://www.wearedevelopers.com/jobs/ext/3875093-senior-ai-developer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Developer - **Company:** Interiors Group - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Cleansing, Data Infrastructure, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Tensorflow, Standard Sql, Cloud Platform System, Pytorch, Apache Spark, Cloudformation, Data Lakes, Pyspark, Machine Learning Operations, Terraform, Data Pipelines, Docker, Databricks - **Published:** October 22, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pthljmmqiq ## About the Role * Strong hands-on development experience with Python * Practical experience with Machine Learning and MLOps * Experience with TensorFlow or PyTorch * Experience automating and orchestrating ML pipelines using MLflow or comparable tools * Strong experience with Databricks or a comparable data platform * Proficiency in Spark/PySpark and SQL * Experience with data modelling, ETL/ELT pipelines and data lake or lakehouse architectures * Hands-on experience with Docker and Kubernetes * Experience deploying and monitoring production applications in cloud and edge environments * Strong understanding of monitoring, observability and automated alerting * Strong German skills and English skills for technical documentation Additional relevant experience * AWS cloud services * Infrastructure as Code using Terraform, CloudFormation or comparable tools * OpenTelemetry * Data quality tools such as DQX * Experience with industrial AI, simulation models or control systems ## Description * Enhance the existing ML training pipeline and automate model retraining * Integrate automated retraining into existing support processes * Develop and validate simulation models for industrial filling processes * Automate the optimisation of control parameters within simulations * Validate simulation results on a physical test setup * Monitor data quality, data drift and control-loop performance using Databricks * Extend existing monitoring dashboards and implement automated alerts * Establish real-time monitoring for edge AI services using OpenTelemetry * Collect, visualise and monitor application metrics and logs * Develop monitoring and alerting concepts covering edge and cloud environments