Senior AI Software Engineer

Bosch Group•Northville Charter Township, MI

About The Position

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.

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 Haves

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.

Responsibilities

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.
Build, secure, and deploy high-performance APIs and micro-services on Microsoft Azure using server-less workflows (e.g., Azure Functions).
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.
Programmatically interface with commercial and open-source LLMs through Python-based APIs, establishing reliable workflows for caching, rate-limit handling, and token usage optimization.
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.
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.
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.
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).
Adhere strictly to internal software quality processes and industry best practices, including rigorous peer code reviews, automated testing, continuous integration, and compliance standards.