AI Application Engineer

Tasks

• Design and implement scalable, production-grade AI/ML solutions based on AI use case requirements from internal customers.
• Own agentic AI / ML system lifecycle from design to deployment, monitoring, and continuous improvement.
• Build and maintain software services to serve AI agents / ML models and integrate into KION’s software data ecosystem.
• Write clean, maintainable, and high-quality code, ensuring alignment to industry best practices.
• Develop and enforce best practices in system design, software architecture, API design and coding standards and security.
• Collaborate with data engineers and cloud engineers to establish AI/ML development environments for efficient model training, evaluation, streamline the model deployment process, including dependency and version management, and utilize cloud platforms (GCP preferred) when possible.
• Collaborate with business and IT to develop GenAI based applications, supporting RAG, multi-agent, multimodal systems leveraging modern frameworks.
• Build and maintain robust CI/CD workflows for AI/ML applications.
• Partner with AI enablement teams across the organization as well as KION IT Cloud Infrastructure, AI Platform, and security and data privacy teams to ensure AI/ML capabilities align with IT framework and guidelines within KION.

Qualifications

• BS/MS in Computer Science, Software Engineering, AI/ML, or a related field.
• 3+ years of experience in AI / ML engineering, micro service and API development, or related fields.
• Proven experience deploying and supporting agentic AI / ML models into production systems with SLA.
• Strong experience with cloud platforms (preferably GCP) and related tools: Docker, Kubernetes, edge computing.
• Experience working in agile development environments, delivering incremental enhancements that drive customer value.
• Deep understanding of APIs, software integration, and system architecture in enterprise-scale environments.
• Hands-on experience with GenAI application development and performance evaluation.
• Working knowledge of common AI/ML algorithms, and LLMs (Large Language Models).
• Proficiency in Python (preferred) or other general-purpose programming languages.
• Familiarity with orchestration tools: LangGraph, Airflow, Kubeflow, etc.
• Strong DevOps and CI/CD tools.
• Proficiency in cloud-based architecture and experience with infrastructure automation (Terraform, Kubernetes).