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Strong brand, metro location, mid-level ML/GenAI role with broad skills and high visibility increases applicant competition.
Core ML, data engineering and GenAI skills are transferable, though platform and agentic-AI experience add moderate domain bias.
Explicit 4+ years requirement plus mandatory programming, cloud, data platform and GenAI/ML production skills makes screening strict.
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Design and build scalable, trusted data and AI architecture including reusable data products and production-grade ML and GenAI solutions.
Develop data pipelines (batch, streaming, near-real-time), ensure data quality, governance, security, and enable data democratization for internal consumption.
Contribute to architecture decisions, engineering best practices, and mentor engineers to build a scalable AI-driven supply chain platform.
4+ years of experience in software engineering, data engineering, AI/ML engineering or related fields.
Strong programming skills in Java and Python; knowledge of Scala, Kotlin is a plus.
Experience with scalable, distributed data-intensive systems and leading architecture/design for data or AI platforms.
Experience with data pipeline technologies such as Kafka, Spark, Databricks, Flink, and cloud platforms like Azure, AWS, or Google Cloud.
Experienced in integrating AI/ML models into production environments including GenAI/LLMs, agentic AI workflows, and AI security guardrails.
Technical leader with a platform mindset focusing on reusable capabilities over one-off solutions and strong architecture contribution.
Domain familiarity with data-intensive systems and distributed architectures, preferably with exposure to supply chain or logistics platforms (good to have, but not mandatory).