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Tier-1 brand plus mid-level experience and metro location increases applicant competition.
ML infrastructure skills are transferable across industries but require domain expertise in LLMs and retrieval.
Explicit 5–7 years and specific ML, MLOps, LLM, and vector DB requirements raise strictness.
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Lead design, build, and operation of scalable AI/ML platforms supporting search, retrieval, recommendation, and Context Graph capabilities across Autodesk's Industry Clouds.
Architect and manage large-scale model inferencing pipelines ensuring low latency, high reliability, and cost efficiency.
Drive AI automation workflows, mentor engineers, and define AI/ML engineering best practices within the team.
5-7 years of experience building and running ML systems or AI-powered applications.
BS or MS in Computer Science, Machine Learning, or related field, or equivalent work experience.
Expert proficiency in Python for ML engineering; familiarity with Java or other backend languages.
Proven experience with LLM-powered applications including RAG architectures, agents, or fine-tuning pipelines; familiarity with MLOps platforms, vector databases, and cloud infrastructure (AWS).
Experienced with end-to-end ML systems integrating search and Context Graph technologies in a cloud environment.
Able to collaborate with cross-functional leadership to define AI strategy and implement scalable technical solutions.
Skilled at mentoring engineers and improving engineering practices in AI/ML development context.