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Mid-level seniority, metro location, and broad ML skillset increase applicant competition moderately.
Core ML/AI skills transfer across industries but specialized model deployment and MLOps needs moderate domain fit.
Explicit 5+ years plus many mandatory ML, MLOps, and cloud skills create strict shortlisting filters.
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Develop and implement AI/ML models on Google Cloud Platform with TensorFlow, Keras, and related technologies.
Deploy, monitor, and maintain machine learning models in production environments including CI/CD and MLOps practices.
Handle supervised, unsupervised, and reinforcement learning problems across multiple industry verticals and transform data science prototypes into scalable products.
Minimum 5 years of experience in Machine Learning and predictive analytics including hands-on model development using TensorFlow, Keras, PyTorch on GCP AI/ML services.
Strong Python programming skills with experience in Python SDK, Spark (PySpark), and libraries like NumPy, Pandas, scikit-learn.
Experience with model deployment and monitoring on public clouds (AWS/Azure/GCP), familiarity with Kubernetes container orchestration.
Work Experience Required: Minimum 5 years in relevant AI/ML and cloud-based machine learning engineering.
Experienced in applying AI/ML solutions specifically on Google Cloud Platform using services like Vertex AI, BigQuery, and Cloud Composer.
Skilled in software engineering best practices for machine learning including development of production-ready scalable libraries and implementation of CI/CD in ML workflows.
Versed in statistical analysis, text mining, computer vision, and capable of addressing complex ML problems across diverse sectors such as banking, finance, telecom, retail, and technology.