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Remote posting, known employer brand, and mid-senior ML specialization create moderate applicant competition.
Skills are domain-specific to ML production and less transferable outside ML-heavy engineering roles.
Multiple explicit years plus mandatory ML, cloud, and MLOps tech requirements enforce strict shortlisting.
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Own architecture and management of machine learning production lifecycle including deployment, automation, and monitoring of ML models in a scalable, secure cloud environment.
Design and maintain CI/CD/CT pipelines for ML models, incorporating best practices in MLOps and ensuring performance, cost-effectiveness, and compliance via model versioning and lineage tracking.
Collaborate cross-functionally with Data Scientists, Software Engineers, and Product teams to integrate ML solutions and mentor ML engineering staff while providing technical documentation.
5+ years experience deploying and scaling ML solutions on cloud platforms (AWS/GCP/Azure) using tools like SageMaker, Glue, Lambda, Docker.
7+ years programming experience in Python, Scala or similar AI/ML languages.
4+ years developing deep learning and traditional ML models with frameworks such as PyTorch, TensorFlow, Huggingface, scikit-learn.
Degree in Computer Science, Engineering, or related field or equivalent practical experience.
Experienced in end-to-end MLOps ownership including automation, monitoring, and governance in production AI environments.
Demonstrates a strong ability to bridge data science and software engineering disciplines and to operate cross-functionally in product-driven teams.
Up-to-date with current generative AI technologies and capable of providing architectural guidance and mentorship to ML engineering teams.