Senior Engineer AWS AI & MLOps

Your responsibilities, MLOps & Cloud Architecture, , • Design and own end-to-end MLOps architecture on AWS for autonomous driving workloads, , • Architect multi-zone, highly available ML platforms supporting urban L2++ hands-off use cases, , • Build and operate scalable multi-GPU training environments using Ray clusters, , • Define infrastructure standards for compute management, networking, storage, and security, , AWS Platform & Infrastructure, , • Implement and manage:, o Amazon EKS / Kubernetes (K8s) for ML workloads, o VPC architecture, subnets, routing, and network isolation, o S3 Intelligent-Tiering for cost-efficient storage of large-scale sensor and training data, o AWS Lambda for event-driven ML workflows and automation, o AWS IoT infrastructure provisioned via Terraform, , • Ensure strong multi-zone resilience, fault tolerance, and disaster recovery strategies, , MLOps Pipelines & Tooling, , • Design and operate ML pipelines using:, o Apache Airflow for orchestration, o MLflow for experiment tracking, model versioning, and lifecycle management, , • Implement CI/CD pipelines for ML and infrastructure using GitHub, , • Enable reproducible, traceable, and auditable ML workflows aligned with automotive standards, , Machine Learning Engineering, , • Enable scalable data ingestion and processing pipelines for sensor-rich datasets, , • Establish data quality checks, validation frameworks, and train/test split governance, , • Support ML teams with optimized workflows for training, evaluation, and deployment, , • Collaborate on best practices for training at scale, including performance tuning and cost optimization, , Algorithmic & Domain Collaboration, , • Work closely with ML researchers and engineers on:, o Perception algorithms (vision, sensor fusion, object detection, tracking), o Behavioral and decision-making algorithms, , • Translate algorithmic requirements into production-ready infrastructure, , • Apply automotive domain knowledge to ensure platform suitability for safety-critical systems, , Observability, Scalability & Operations, , • Build strong monitoring, logging, and observability for ML systems and infrastructure, , • Enable performance metrics, failure detection, and operational insights across the ML lifecycle, , • Continuously improve platform scalability, reliability, and operational efficiency What we offer, Private health care (Signal Iduna) and Life insurance for you and your beloved ones, Well-Being Program that includes regular webinars, workshops, and networking events, Hybrid work (min. 47 days/yr of remote work, flexible working hours), Employee Pension Plan paid by the employer (you get + 3,5% on each gross salary), Access to sports groups and Multisport card