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Mid-level ML role, metro location, and reputable global brand increase applicant competition.
Role requires specialized ML, AWS, and production deployment experience, so industry transferability is limited.
Explicit 3-6 years, mandatory ML production experience and specific AWS/ML stack requirements raise strictness.
Build, train, and deploy end-to-end machine learning models independently on complex projects.
Manage and configure AWS infrastructure for ML workflows including VPC, EC2, EKS, EMR, S3, and RDS.
Design robust ML pipelines from data processing to model serving and collaborate across teams to translate business needs into technical solutions.
Bachelor's or Master's degree in Engineering, Mathematics, Statistics, or related field.
3-6 years professional experience in machine learning engineering with production ML model deployment.
Proficiency with Python, SQL, and distributed computing frameworks like Spark.
Practical experience with AWS services specifically VPC, EC2, EMR, RDS, S3, and EKS for ML workloads.
Experienced working autonomously on complex ML projects and designing scalable ML system architectures.
Hands-on with modern AI frameworks and techniques including LLMs or Generative AI.
Skilled in end-to-end ML pipeline design, AWS infrastructure management, and cross-functional collaboration.