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Metro location and visible senior role increase competition, but niche GCP MLOps/LLMOps specialization limits applicant pool.
Requires specialized AI platform, GCP, MLOps and LLMOps expertise, limiting transferability across industries.
Explicit 10-12 years plus mandatory 4-5 years AI platforms and deep GCP/MLOps expertise make filters stringent.
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Lead design and implementation of scalable AI, data, and automation platforms on Google Cloud Platform with strong focus on security, governance, and cost efficiency.
Drive operationalization and governance of AI solutions including MLOps, LLMOps, and AgentOps for production readiness and reliability.
Mentor and lead junior engineers, review technical designs and code, establish engineering standards, and ensure high-quality delivery of AI platform initiatives.
10-12 years of overall technology experience including 4-5 years hands-on in AI engineering or AI platforms engineering focused on Google Cloud Platform and AI solution delivery.
Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, or related field; Master's degree preferred.
Strong technical expertise in GCP services such as Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, IAM and network security.
Proven experience in MLOps, LLMOps, AgentOps, security and cloud governance standards for AI platforms, including operational support and incident management.
Deep expertise in designing and operating enterprise AI platforms on Google Cloud, integrating automation, data, AI/ML, and security governance capabilities.
Experienced technical leader able to mentor engineers, define reusable engineering patterns, and enforce coding and architecture standards.
Comfortable leading complex cloud-native AI architectures involving generative AI, AI agents, and multi-agent orchestration under enterprise constraints and cost control.