AI Developer, Digital Enterprise

Job description

Job descriptions may display in multiple languages based on your language selection. What we offer: At Magna, you can expect an engaging and dynamic environment where you can help to develop industry-leading automotive technologies. We invest in our employees, providing them with the support and resources they need to succeed. As a member of our global team, you can expect exciting, varied responsibilities as well as a wide range of development prospects. Because we believe that your career path should be as unique as you are. Group Summary: Magna is more than one of the world’s largest suppliers in the automotive space. We are a mobility technology company built to innovate, with a global, entrepreneurial-minded team. With 65+ years of expertise, our ecosystem of interconnected products combined with our complete vehicle expertise uniquely positions us to advance mobility in an expanded transportation landscape. Job Responsibilities: We are seeking a highly skilled AI Developer to join our team and drive the design, implementation, and scaling of advanced AI solutions. This role combines in-depth coding expertise with a strong systems engineering mindset: you’ll own the execution of end-to-end AI platform lifecycle—from infrastructure, and MLOps pipelines to production deployment and ongoing performance optimization. You’ll collaborate closely with solutions architects, data scientists, DevOps engineers, and product owners to turn models into reliable, secure, and maintainable services at enterprise scale. Major Responsibilities • Design, build, and maintain AI/ML platform components (training clusters, feature stores, inference services, GenAI, vision, RAG, NLP). • Develop and manage infrastructure-as-code for scalable deployments (Terraform, ARM template) • Implement and operate CI/CD pipelines for automated model training, testing, and deployment • Integrate AI services with existing systems via APIs, messaging, and data pipelines • Monitor performance, reliability, and model drift; establish alerting and incident-response runbooks. • Optimize system performance through capacity planning, performance tuning, and disaster-recovery strategies • Write, and maintain high-quality, reusable code and libraries (C#, Python, Java) • Collaborate with cross-functional teams (Architect, Data Science, DevOps, Security) and document system designs and SOPs. Knowledge and Education            Completion of University Degree • Bachelor’s or Master’s degree in Computer Science, Electrical/Computer Engineering, or related field Work Experience   3 to 5 Years of Work Related Experience • 3+ years in software engineering roles, with 2+ years building and operating AI/ML platforms • Proven track record delivering production-grade AI services at scale Skills and Competencies Required to Perform the Job • Cloud Platforms: Proficiency with various cloud platform services for compute, storage, and networking (e.g. AWS, Azure etc.) • Containerization & Orchestration: Hands-on experience with Docker, Kubernetes, Helm • Strong skills and knowledge in distributed system architecture, integration, software design patterns, and object-oriented programming concepts and patterns. (Microservices, Monolith, Modular Monolith, containerized applications, etc.) • MLOps: Building and managing pipelines using GitLab CI, Argo CD, MLflow • Programming Languages: Advanced Python (including async and multithreading); familiarity with C#, Python, or Java. • LLM Application & Agent Frameworks: Experience designing and implementing RAG/agentic workflows using LangChain and LangGraph (tooling, chaining, memory, orchestration) • Machine Learning Frameworks: Deep knowledge of TensorFlow, PyTorch, scikit-learn • Data Engineering: Experience with Kafka, Azure Data Factory, Databricks, and Beam for data ingestion and processing • Monitoring & Observability: Implementing observability stacks (Azure Monitor, Prometheus, Grafana, ELK) and alerting • Security & Compliance: Securing data pipelines, understanding of GDPR/CCPA implications • Cross-Functional Collaboration: Effectively partner with Data Science, DevOps, Security, and Product teams • Technical Documentation: Clear authoring of runbooks, SOPs, and API specs • Stakeholder Communication: Translating technical concepts into business terms for executives and non-technical audiences • Presentation Skills: Leading demos, technical deep dives, and knowledge-sharing sessions • Continuous Learning: Staying current with emerging AI/ML, MLOps, and platform engineering trends Working Conditions and Environment