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Tier-1 brand, metro location, and mid-level generalist ML title increase candidate competition.
Specialized MLOps, Vertex AI, and LLM/GenAI platform experience reduces cross-industry transferability.
Explicit 4–6 years plus mandatory GCP, Kubernetes, Python, and LLMOps tooling creates strict filters.
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Develop and maintain AI-powered platform frameworks and internal tooling deployed on GKE Kubernetes clusters using GitOps workflows.
Build and troubleshoot AI/ML and GenAI pipelines and workflows using GCP services such as Vertex AI, Dataflow, and Composer, integrating enterprise security tools.
Implement monitoring, observability, and evaluation frameworks for LLMs and AI/ML models including model quality, safety, and cost optimization.
Bachelor’s Degree in Computer Science or relevant experience.
4–6 years of experience in cloud-native software engineering.
Strong hands-on experience with GCP services (Vertex AI, GKE, Dataflow, Dataproc, Composer) or equivalent cloud platforms.
Proficiency in Python and Kubernetes, including expertise in GitOps deployment tools such as ArgoCD.
Experienced in production support of AI/ML models with skills in monitoring, debugging, and retraining loops.
Familiarity with LLMOps toolchains including RAG pipelines, agent frameworks, and prompt/version management.
Able to collaborate across platform and security teams to integrate enterprise-grade security and observability tooling into AI/ML environments.