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Mid-level generalist ML/data role with broad requirements and known employer increases competition.
Core ML and data platform skills transfer across industries, but supply-chain domain knowledge is preferred.
Explicit 4+ years plus specific ML, data platform and cloud stack requirements make filters strict.
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Design and build scalable data and AI architecture for data-intensive systems supporting supply chain digitalization.
Develop reusable data foundations, data products, and AI capabilities including ML, GenAI, and agentic AI solutions with appropriate guardrails and monitoring.
Lead architecture, engineering practices, and mentor engineers to establish standards in building production-ready, reusable data and AI platform capabilities.
4+ years experience in software engineering, data engineering, AI/ML engineering, or related fields.
Strong programming skills in Java and Python; knowledge of Scala, Kotlin or similar is a plus.
Experience with data pipelines, distributed data-intensive systems, and technologies like Kafka, Spark, Databricks, Flink, and relational/NoSQL databases.
Experience or exposure to GenAI/LLM solutions, AI agents, AI security, and at least one cloud platform (Azure, AWS, or Google Cloud).
Experienced in designing and owning architecture for scalable, distributed AI and data platforms with a focus on reusable, production-ready solutions rather than one-off projects.
Comfortable working cross-functionally with engineers, data scientists, architects, and domain experts to build integrated AI and data capabilities for supply chain contexts.
Demonstrates strong software engineering discipline including CI/CD, automated testing, monitoring, and platform mindset emphasizing scalability, reliability, and security.