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Tier-1 brand, generalist title, mid-level experience and broad skillset increase competition.
Specialized ML and data platform skills transfer across industries, though domain knowledge adds some bias.
Explicit 4+ years plus mandatory ML/data platform, cloud and specific technologies raises shortlisting strictness.
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Design and build scalable data and AI architecture, including data foundations, pipelines, and reusable data products.
Develop and productionise machine learning and GenAI/LLM solutions, incorporating AI agents and implementing security guardrails and monitoring.
Establish engineering standards, take key architecture decisions, and mentor engineers to build a platform enabling supply-chain intelligence applications.
Minimum 4+ years of experience in software engineering, data engineering, AI/ML engineering or related field.
Strong programming skills required in Java and Python; knowledge of Scala, Kotlin or similar is a plus.
Experience with distributed data systems, data modelling, data pipelines, and platforms using technologies like Kafka, Spark, Databricks, Flink.
Hands-on experience with productionising ML models, GenAI/LLM applications, and knowledge of cloud platforms such as Azure, AWS or Google Cloud.
Proven experience contributing to or owning architecture and technical design for scalable data and AI platforms.
Experience building production-grade AI solutions including ML, GenAI/LLM, and agentic AI workflows with security and operational best practices.
Strong platform mindset focused on reusable, scalable capabilities rather than one-off projects, preferably with exposure to supply-chain or logistics domains.