Senior AI Software Engineer

Mobility is experiencing a fundamental change, with software taking center stage. We enable our customers to master the complexity of the entire software lifecycle, thus contributing to the vision of the fully programmable vehicle of the future.

ETAS’ products and solutions include vehicle basic software, middleware, development tools, cloud-based operations services, cybersecurity solutions as well as end-to-end engineering and consulting.

Founded in 1994, ETAS is a subsidiary of Robert Bosch. With headquarters in Germany, we operate globally, serving customers and partners around the world.

At ETAS, we look for motivated individuals with initiative and drive. We offer superb benefits that extend beyond our excellent compensation package and bonus plan. ETAS values the balance between personal and professional growth and company interests. If you are interested in working in a highly technical, fast-paced and fun environment, we'd like to hear from you! Welcome to ETAS!

We are seeking an experienced and innovative Senior AI Software Engineer to develop and deploy our next-generation, production-grade AI systems. In this role, you will be responsible for orchestrating robust Python-based back-end architecture, integrating state-of-the-art Large Language Models (LLMs), optimizing high-performance vector databases, and deploying scalable, secure APIs natively on Microsoft Azure. You will work closely with our Original Equipment Manufacturers (OEM) customers to agree on new requirements, develop plans to deliver solutions using Agile processes, and ensure strict adherence to software quality standards throughout the development lifecycle.

AI Systems Development

Architect, write, and maintain clean, production-grade, and testable AI-driven software and back-end services using Python . Implement comprehensive testing, validation, and evaluation strategies to ensure the safety and reliability of model outputs.

API & Cloud Deployment

Build, secure, and deploy high-performance APIs and micro-services on Microsoft Azure using server-less workflows (e.g., Azure Functions).

Vector Search & Retrieval

Integrate, structure, and optimize vector databases (e.g., PGVector, Qdrant, Milvus, or Azure AI Search) to power Retrieval-Augmented Generation (RAG) pipelines and high-speed semantic search.

LLM Integration

Programmatically interface with commercial and open-source LLMs through Python-based APIs, establishing reliable workflows for caching, rate-limit handling, and token usage optimization.

Big Data & Pipelines

Design and implement scalable data processing pipelines on platforms like Azure Databricks or Microsoft Fabric . Manage data progression through a Medallion Architecture to power AI systems.

MLOps & Deployment

Own the end-to-end ML lifecycle on Azure. Architect and maintain CI/CD pipelines in Azure DevOps , using Docker and Azure Kubernetes Service (AKS) to create scalable and reproducible ML workflows.

Customer & Requirement Management

Partner directly with OEM customers to elicit, clarify, and agree upon technical requirements. Translate customer business needs into structured technical plans and deliver high-quality solutions to those specifications.

Agile Delivery & Workflows

Active participation in Agile/Scrum methodologies . Use version control ( Git ) daily and manage development workflows, tasks, and documentation using tools like Jira or Microsoft Azure DevOps (ADO) .

Quality Process Adherence

Adhere strictly to internal software quality processes and industry best practices, including rigorous peer code reviews, automated testing, continuous integration, and compliance standards.

Requirements

Several years of professional experience developing, maintaining, and deploying complex, data-driven AI systems.; Expert-level proficiency in Python, including experience with asynchronous development and modern API frameworks.; Proven hands-on experience deploying APIs to Azure, managing Azure App Services, Azure serverless functions, Azure storage accounts, and cloud security configurations.; Direct experience designing and querying vector databases and indexing systems for production semantic search and semantic caching.; Solid foundation in calling LLMs through Python APIs, manipulating context windows, structuring prompt templates, and managing payloads.; Strong communication and collaborative skills to interface directly with external partners/OEMs, negotiate requirements, and deliver technical solutions to customer expectations.; Direct experience working in an Agile/Scrum environment. Advanced knowledge of Git for version control (branching, merging, pull requests) and task management workflows in Jira or Microsoft Azure DevOps .; Demonstrated commitment to following software quality processes, writing unit/integration tests, and maintaining robust CI/CD principles.

Nice to have

Experience with Azure Machine Learning (Azure ML) , containerizing applications with Docker , orchestrating with Azure Kubernetes Service (AKS) , and building CI/CD pipelines in Azure DevOps .; Hands-on experience with Azure data platforms like Azure Databricks or Microsoft Fabric . Understanding of Medallion Architecture is strongly preferred.; Practical knowledge or experience designing autonomous agentic workflows and multi-agent orchestration frameworks (such as CrewAI, AutoGen, or LangGraph) to automate multi-step reasoning tasks.; Experience evaluating, comparing, and switching between different LLMs (e.g., GPT, Claude, Llama, Gemini) to optimize latency, cost, and accuracy for specific use cases.