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Strong employer brand plus mid-level, in-demand LLM/MLOps skills increases candidate competition.
Core ML and MLOps skills transfer across industries, but context-graph and vector DB expertise raise domain specificity.
Explicit 5-7 years and mandatory ML, LLM, MLOps, vector DB, and cloud requirements raise shortlisting strictness.
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Lead design, development, and operation of AI/ML platforms powering search, retrieval, and Context Graph capabilities across Autodesk Industry Clouds.
Architect and manage scalable, low-latency model inferencing pipelines ensuring high reliability and cost efficiency.
Drive AI automation workflows and best practices; mentor engineers and collaborate with cross-functional leadership to implement AI strategy.
Bachelor’s or Master’s in Computer Science, Machine Learning, or related field, or equivalent experience.
5-7 years of experience building and operating ML systems or AI-powered applications.
Proficiency in Python for ML engineering; experience with ML fundamentals including model training, evaluation, inference, and production deployment at scale.
Experience with LLM-powered applications, MLOps platforms (e.g. MLflow, Kubeflow, SageMaker Pipelines), vector databases (Pinecone, Weaviate), cloud platforms (AWS SageMaker, Lambda), and Information Retrieval tools (Lucene, ElasticSearch).
Experienced in designing and deploying large-scale AI/ML systems for production environments with strong operational focus.
Demonstrates ability to lead AI/ML integration and set technical direction within cross-disciplinary teams including Product and SRE stakeholders.
Knowledgeable in context graph technologies, AI evaluation frameworks, and multi-agent AI orchestration tools is advantageous.