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Mid-level ML role, metro location, popular title and broad GenAI/GCP requirements increase applicant competition.
Requires specialized GenAI, MLOps and GCP experience, moderately limiting cross-industry portability.
Explicit 3–6 years plus GCP, GenAI, MLOps and production experience increases filtering strictness.
Build, deploy, and maintain production-grade AI/ML solutions on Google Cloud Platform for Fortune 500 clients.
Design and implement generative AI applications including RAG systems, agentic workflows, multi-agent orchestration, and prompt engineering.
Own the complete ML lifecycle including CI/CD, automated testing, model validation, production APIs, and monitor systems for reliability and drift.
3-6 years hands-on ML engineering experience with generative AI and multiple ML domains.
Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (or equivalent experience).
Expert-level Python skills with software engineering fundamentals such as API design, testing, containerization.
Proven experience shipping production ML systems in cloud environments, specifically Google Cloud Platform (Vertex AI, BigQuery, Cloud Run) or equivalent.
Experienced in generative AI and hybrid ML solutions including retrieval-augmented generation, multi-agent orchestration, and knowledge graphs.
Capable of full ML lifecycle ownership with strong focus on production reliability, MLOps, and client-facing technical communication.
Comfortable working in enterprise-scale environments involving collaboration with architects, data engineers, and business analysts.