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Metro location, popular AI title, broad skills requirements, and inferred mid-level seniority increase competition.
Core ML engineering skills transfer across industries, but industrial-domain experience is a moderate advantage.
Mandatory ML production, system design, and API integration skills but no explicit years, so medium strictness.
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Design and implement scalable AI features including copilots, Retrieval-Augmented Generation (RAG), search, and other AI capabilities within an industrial context.
Build efficient end-to-end data pipelines handling large-scale industrial data with low latency and high reliability.
Integrate AI services into Digital Twin ecosystems ensuring seamless user experience and collaborate with cross-functional teams to deliver production-ready AI solutions.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Professional experience building and deploying AI/ML-driven solutions.
Strong foundation in data structures, algorithms, complexity analysis, and system design.
Experience designing and implementing scalable APIs and services in modern full-stack environments; MEAN stack experience is a strong plus.
Demonstrated capability to deliver end-to-end AI solutions integrating with web and mobile platforms in industrial or complex technical environments.
Experience collaborating effectively with AI researchers, product teams, and domain experts translating AI research into practical, scalable applications.
Balance theoretical knowledge with practical deployment needs, emphasizing performance, efficiency, and robustness in industrial AI applications.