Back
Mid-level ML role at a recognizable software brand attracts many qualified applicants, increasing competition.
Core ML infra and MLOps skills transfer across industries, though Context Graph specifics add moderate domain bias.
Explicit 2–4 year requirement plus specific ML, vector DB, cloud and MLOps skills makes shortlisting highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Build and maintain ML model evaluation frameworks and inferencing pipelines to ensure quality, relevance, low latency, and high reliability of AI models within search and Context Graph systems.
Develop AI automation workflows to accelerate ML feature development, testing, and deployment across Autodesk Industry Clouds.
Collaborate with engineering teams to integrate AI/ML capabilities into scalable microservices and participate in on-call rotation to manage production incident recovery.
Bachelor's or Master's degree in Computer Science, Machine Learning, or related field.
2-4 years of experience building and running ML systems or AI-powered applications.
Proficiency in Python and machine learning fundamentals including model training, evaluation, inference, and deployment.
Experience with LLM APIs, foundation models, vector databases (e.g., Pinecone, Weaviate), and cloud platforms such as AWS (SageMaker, Lambda, ECS).
Experienced in scalable AI/ML system design and operation with hands-on knowledge of vector embedding and semantic search technologies.
Operates effectively in a collaborative software engineering environment integrating AI/ML solutions within microservices and cloud infrastructure.
Capable of simplifying and articulating complex AI/ML problems, focused on delivering intelligent solutions to improve user experiences.