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Strong Tier-1 brand, mid-level generalist AI role, metro location, and popular LLM skillset increase competition.
AI/ML and MLOps skills are fairly transferable across industries, though specialized LLM and FinOps experience reduces portability slightly.
Explicit 4–7 years, mandatory cloud/AI certification, and strict GCP/LLM/MLOps tech requirements make shortlisting strict.
Architect, build, and deploy production-grade AI products automating engineering workflows and accelerating software transformation.
Design and implement scalable AI systems utilizing NLP and LLMs for legacy codebase modernization and technical documentation automation.
Manage AI product lifecycle including deployment on Google Cloud Platform with performance optimization and cost monitoring.
4 to 7 years of hands-on experience in end-to-end AI/ML product development including generative AI and software automation.
Bachelor's or Master's degree in Computer Science, AI, Data Science, Software Engineering, or equivalent quantitative field.
Must hold at least one recognized cloud or AI certification (e.g., Google Cloud Professional Machine Learning Engineer).
Proficiency with Google Cloud Platform services including Vertex AI, Cloud Run, BigQuery, Cloud Functions, and GKE.
Experienced in deploying complex AI-driven automation solutions in cloud-native environments with a focus on scalability and cost efficiency.
Strong background in generative AI, prompt engineering, LLM frameworks, and modern software engineering practices including MLOps and DevOps.
Capable of translating ambiguous AI research into reliable enterprise-grade platforms and collaborating effectively with cross-functional teams for feature delivery.