Lean digital transformation (LDT) – test equipment LabView connectivity to Cumulus/Microsoft Power BI via API plugins, Power Apps, Microsoft AI Builder and Copilot.
Lean – Collect data and facts for identifying wastes to drive lean improvement (eliminate non-value add process)
Establish an electronic files archiving system and go paperless for documentation
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Stakeholder Communication: Serve as the primary point of contact between the agile pod and stakeholders, facilitating transparency and alignment on project goals and progress.
Coding & Code Reviews: Actively develop core components and conduct rigorous code reviews to ensure adherence to best practices, security, and quality standards.
DevOps Integration: Implement and manage DevOps practices within the team, utilizing relevant tools to streamline development and deployment processes.
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Apply AI for document processing, data extraction, classification, decision support, and workflow optimization; ensure solutions are measurable (cycle time, accuracy, cost savings)
Build and maintain system integrations between AI solutions and enterprise systems (e.g. finance/accounting platforms, and—where applicable—TMS/WMS etc.) via APIs, files, or EDI
Work with internal IT/security teams to ensure appropriate access control, data privacy, and compliance requirements are met for each solution
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Specify and develop new toolchain features for existing and new NPU architectures, working with the inference engine sub-team
Enhance, maintain and support existing toolchain features, and modify model network layers for NPU optimality, working with the application engineering teams and sometimes customers/partners.
Constantly improve productivity through automation in all areas
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Establish the runtime and governance framework for AI Agents, including execution isolation, permission control, auditability, evaluation, stability, and cost governance.
Collaborate with product, engineering, QA, and operations teams to standardize, productionize, and scale platform capabilities, continuously improving engineering efficiency and delivery quality.
Bachelor’s degree or above, preferably in Computer Science, Software Engineering, Artificial Intelligence, or related fields.
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Support the engineering deployment of AI/LLM capabilities based on business needs, such as integrating LLM APIs, building basic RAG pipelines, and embedding tool/agent capabilities into existing systems.
Help build and maintain datasets and benchmarks for evaluation/regression, track online performance, and assist with debugging and fixing issues.
Produce necessary technical documentation and communicate effectively across teams.
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Apply AI-assisted development tools such as GitHub Copilot, along with spec-driven development practices, to improve productivity and delivery quality.
Build scalable applications using Java, Spring, databases, messaging, and javascript technologies.
Contribute to technical design, architecture discussions, code reviews, and engineering best practices.
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