AI Engineer

Enterprise Technology plays a critical part in shaping the future of mobility. If you’re looking for the chance to leverage advanced technology to redefine the transportation landscape, enhance the customer experience and improve people’s lives, this is the opportunity for you. Join us and challenge your IT expertise and analytical skills to help create vehicles that are as smart as you are.
We are seeking a creative and driven Full-Stack Software Engineer to join our innovative team. You will be instrumental in designing, building, and deploying robust, scalable, and intuitive applications that leverage Generative AI and cloud-native technologies on the Google Cloud Platform (GCP).
This is a role where you will tackle exciting challenges across the entire stack. You won't just be writing code; you will be a key contributor to architectural decisions, product strategy, and the overall technical direction of our AI-powered initiatives.
AI & ML Development: Design, train, and deploy a variety of AI solutions, including classical machine learning models (classification, regression, clustering) and advanced Generative AI workflows.
Agentic Systems: Develop and productionalize AI agents capable of multi-step reasoning, tool-use, and integration with external APIs.
Software Integration: Architect and build production-ready code to integrate AI/ML models into user-facing applications via RESTful APIs and microservices.
Pipeline Engineering: Build and maintain scalable data and ML pipelines to automate model training, deployment, and versioning.
MLOps & Governance: Implement model monitoring and governance frameworks to track performance, drift, and reliability in production.
Cloud Engineering: Utilize GCP services to build, host, and scale AI applications in a serverless or containerized environment.
Collaboration: Work within an Agile framework, collaborating closely with Product Managers and other engineers to deliver business value for our commercial customers.
Required Skills & Experience
Education: Bachelor’s Degree in Computer Science, Data Science, Statistics, or a related technical field.
Experience: 3+ years of professional experience in AI/ML engineering or Software Engineering with a focus on model deployment.
Core ML Knowledge: Strong foundation in machine learning theory and practical experience with libraries such as scikit-learn, XGBoost, or PyTorch/TensorFlow.
GenAI Stack: Hands-on experience with LLMs and orchestration frameworks (e.g., LangChain, LlamaIndex, or AutoGen).
GCP Proficiency: Working knowledge of Google Cloud Platform, specifically Vertex AI, BigQuery, and Cloud Run.
Software Engineering: Proficiency in Python and a solid understanding of software best practices, including API design, Git version control, and unit testing.
Preferred Requirements:
Infrastructure as Code (IaC): Experience with Terraform for provisioning and managing cloud infrastructure.
CI/CD Pipelines: Experience with Tekton or similar cloud-native CI/CD frameworks for automating ML workflows.
Production Experience: Proven track record of taking an AI agent or ML model from a prototype to a stable, monitored production environment.
Communication: Excellent verbal and written communication skills with the ability to explain technical concepts to non-technical partners.
Attitude: A strong desire for continuous learning and upskilling in the rapidly evolving AI landscape.

Work arrangement
Hybrid