Design the foundation that makes intelligent systems scale for our enterprise.
Compensation
Base Pay Range: $200,000 to $260,000 annually
An annual employee bonus program
Responsibilities
Define the end-to-end reference architecture for AI and data/storage systems across Azure AI Foundry, Azure AI Services, Azure AI Search, Azure Databricks/Fabric, and Snowflake.
Design integration blueprints for RAG workflows, data pipelines, agentic frameworks, and our enterprise GPT solution.
Establish standards for model deployment, versioning, prompt management, and telemetry within Azure AI Foundry.
Build and maintain scalable cloud environments for model training, evaluation, and inference.
Oversee vector database design (Azure AI Search or equivalent) and integration with Snowflake/Fabric data hubs.
Lead AI Engineer responsible for setting vision, building team, and delivering AI solutions at scale in semiconductor manufacturing.
Responsibilities
Define AI strategy and architecture for integrating machine learning into core engineering and manufacturing processes.
Partner with tool, process, and applications engineers to map as-is processes and define a to-be AI/automation architecture and deliver measurable outcomes.
Ship agentic assistants for use-cases. Stand up LLM + RAG + tool integrations that help engineers accelerate tool operation/setup/maintenance and explain trade-offs.
Lead projects across predictive maintenance, computer vision, LLM-based engineering assistants, process optimization, simulation and digital twins.
Build retrieval-augmented AI assistants to query internal knowledge bases, tools, and logs.
Lead end-to-end design, development, deployment, and adoption of scalable data science and AI solutions across multiple business domains.
Responsibilities
Lead the end-to-end lifecycle of advanced analytics, machine learning, Generative AI, and Agentic AI solutions
Translate ambiguous business problems into well-defined data science hypotheses, solution designs, success metrics, and scalable implementation roadmaps
Develop and maintain advanced predictive/forecasting models for sales, revenue, expense management, and cash-flow forecasting
Design, architect, train, and deploy advanced machine-learning frameworks and traditional time-series models
Own model validation, explainability, reliability, documentation, monitoring, and governance
Requirements
Bachelor’s or Master’s degree in computer science, Data Science, Information Technology, or a related technical discipline
Nice to have: experience in healthcare, commercial, supply chain, or financial analytics environments
Nice to have: good understanding of statistical forecasting methodologies
Senior Data Scientist to lead end-to-end design, development, deployment, and adoption of scalable data science and AI solutions across multiple business domains.
Responsibilities
Lead the end-to-end lifecycle of advanced analytics, machine learning, Generative AI, and Agentic AI solutions
Translate ambiguous business problems into well-defined data science hypotheses, solution designs, success metrics, and scalable implementation roadmaps
Develop and maintain advanced predictive/forecasting models for sales, revenue, expense management, and cash-flow forecasting
Design, architect, train, and deploy advanced machine-learning frameworks and traditional time-series models
Own model validation, explainability, reliability, documentation, monitoring, and governance
Requirements
Bachelor’s or Master’s degree in computer science, Data Science, Information Technology, or a related technical discipline
Nice to have: experience in healthcare, commercial, supply chain, or financial analytics environments
Nice to have: statistical forecasting methodologies
Build and productionize AI-driven demand forecasting, pricing, and optimization models.
Responsibilities
Develop, validate, and iterate on demand forecasting, pricing, and optimization models using large-scale, high-velocity market data.
Explore and analyze data from diverse internal and external sources to uncover signals, define features, and identify opportunities to improve model accuracy and business impact.
Design and run offline experiments, backtests, and online evaluations to measure model performance and profit uplift.
Write production-grade Python code and work with ML and data engineers to productionize models, ensuring they are reliable, observable, and cost-efficient.
Monitor deployed models for drift, accuracy, and data quality issues, and troubleshoot and fix problems quickly.
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