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Senior, niche SLM and supply-chain AI role reduces competition despite metro location.
Strong supply-chain domain, SLM, and production ML requirements limit cross-industry transferability.
Many mandatory technical and domain requirements including 8+ years, SLM expertise, supply-chain and production ML experience.
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Design, fine-tune, and deploy Small Language Models (SLMs) optimized for supply chain AI applications like semantic search, forecasting, and contract intelligence.
Integrate models into production AI products powering enterprise supply chain workflows, ensuring performance, cost-efficiency, and low latency.
Collaborate with domain experts and engineering teams to embed supply chain knowledge, enable AI agentic workflows, and continuously improve model quality and reliability.
8+ years experience in forecasting, optimization, supply chain analytics, or AI-driven product development.
Strong expertise in Small Language Models (SLMs) including parameter-efficient fine-tuning techniques (LoRA/QLoRA).
Proficiency with Python, PyTorch/TensorFlow, GraphQL, vector databases, and deploying models on cloud platforms (AWS, GCP, or Azure).
Deep understanding of supply chain data and workflows (logistics, inventory, procurement, ERP systems) and experience with Knowledge Graphs/Neo4j is mandatory.
Senior data scientist or AI engineer with a product-first mindset focused on operationalizing AI in enterprise supply chain contexts.
Experienced in end-to-end deployment of AI models from prototype to production in robust MLOps environments.
Strategic operator comfortable working closely with cross-functional teams to embed domain knowledge and drive measurable business impact via AI solutions.