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Strong employer brand, popular ML title, mid-level experience band, and metro location increase applicant competition.
Highly specialized agentic AI, Vertex AI and GCP skills limit cross-industry transferability.
Many mandatory specialized ML, MLOps, cloud, and infrastructure skills plus explicit years make shortlisting strict.
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Design, develop, and deploy autonomous and semi-autonomous AI agents using frameworks like LangChain and Vertex AI Agent Builder to automate enterprise processes.
Architect multi-agent orchestration systems and production-grade RAG pipelines integrating Google Cloud services and vector databases to optimize AI agent performance and scalability.
Implement and maintain MLOps infrastructure with CI/CD pipelines, Infrastructure as Code (Terraform), containerized deployments (Docker, Kubernetes/GKE), monitoring, and evaluation metrics for reliable production systems.
Minimum 3 years in Machine Learning or AI Engineering with strong MLOps experience.
1-2 years hands-on experience in designing and implementing LLM-based generative AI or agentic systems in production.
Expert Python programming skills and experience with ML libraries (PyTorch, TensorFlow, or Scikit-learn).
Proven experience with GCP services (including BigQuery, Cloud Run, Cloud Functions, Vertex AI), containerization (Docker, Kubernetes/GKE), Terraform, CI/CD tools, and vector databases (Vertex AI Vector Search, AlloyDB, or equivalents).
Experienced in end-to-end delivery of scalable, production-grade generative AI or agent systems in enterprise environments operating on Google Cloud Platform.
Strong background integrating multi-agent orchestration, RAG pipelines, and advanced prompt engineering to optimize AI agent workflows and reliability.
Skilled in building automated, monitored MLOps pipelines with infrastructure as code and container orchestration to ensure robustness under high load and cost constraints.