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Mid-level popular ML title, metro location, and strong applicant pull for GenAI roles increase competition.
Requires specialist GenAI and MLOps expertise, limiting cross-industry transferability.
Explicit 5–7 years plus specific GenAI, GCP, and MLOps requirements enforce strict filters.
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Design, build, and deploy enterprise-grade Generative AI platform capabilities across multiple business units on GCP and Azure.
Engineer production-ready, reusable GenAI components spanning Decision & Orchestration, Execution Runtime, and Build & Lifecycle layers.
Collaborate with Use Case Implementation Partner and Data & AI teams to ensure reusable, compliant, and timely delivery of AI platform capabilities.
5–7 years of work experience in machine learning engineering with focus on Generative AI and RAG pipelines.
Strong hands-on expertise in GCP AI/ML platform services including Vertex AI, Cloud Run, GKE, BigQuery, and Pub/Sub.
Proficient in Python programming and API development for scalable GenAI service deployment.
Experience implementing AI safety, governance, compliance measures, and CI/CD pipelines for AI platforms.
Experienced in building scalable multi-agent AI systems with orchestration, routing, and session management capabilities.
Skilled in operationalizing GenAIOps and AgentOps frameworks for deployment, monitoring, and governance of AI agents.
Familiar with multi-cloud AI platform delivery (GCP and Azure) and enterprise-scale program governance in regulated BFSI or similar domains.