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Metro location, popular AI engineer title, and broad GenAI skill requirements increase competition.
Generative AI and cloud engineering skills transfer easily across industries.
Explicit 1-3 year requirement plus multiple mandatory GCP, MLOps, and containerization skills makes filtering moderately strict.
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Develop, test, deploy, and maintain AI/GenAI-based SaaS software solutions for enterprise use.
Contribute to the full AI development lifecycle including data handling, model training, evaluation, and deployment.
Participate in design and code reviews, automate testing, and troubleshoot software defects efficiently.
1-3 years experience in object-oriented programming, concurrency, design patterns, and REST APIs.
Experience with CI/CD tools like Terraform and GitHub Actions; familiarity with SQL/NoSQL databases (MongoDB, MSSQL, Postgres).
Exposure to AI/ML, GenAI, MLOps concepts and frameworks such as LangChain and LangGraph.
Experience with GCP services (VertexAI, BigQuery, GKE), Docker, Kubernetes, and testing tools (PyTest, PyMock, xUnit).
Demonstrates practical experience in delivering AI/GenAI product features in a SaaS environment.
Comfortable collaborating in design sessions, code review, and automated testing for enterprise-grade software.
Familiar with cloud-native development and container orchestration using GCP and Kubernetes ecosystems.