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Protocol Intelligence
Data-driven signals on your job's competitivenessSpecialized ML/MLOps role with broad cloud and tooling demands but smaller employer, so moderate competition.
Core ML and MLOps skills transferable across industries, though NLP/CV focus adds specificity.
Multiple mandatory ML, MLOps, and cloud technologies required, making shortlisting stringent.
Job Description
Structured overview of role & requirementsAbout This Role
Build, deploy, and maintain machine learning models for the company’s product suite.
Develop and manage MLOps pipelines leveraging Docker, Kubernetes, and MLflow for reliable model deployment.
Ensure scalability of ML models within AWS cloud environments to support production workloads.
Minimum Requirements
Proficiency in Python and deep learning frameworks such as TensorFlow and PyTorch.
Hands-on experience with MLOps tools including Docker, Kubernetes, and MLflow.
Familiarity with deploying and scaling ML models in AWS cloud environments.
Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experience working end-to-end on ML model development to deployment with operational responsibility.
Comfortable managing containerized applications and orchestration in production-grade MLOps environments.
Knowledgeable in NLP and Computer Vision algorithms indicating specialization in AI domains relevant to products.