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Mid-level metro role with 5–7 years and specialized LLM/GCP skills leads to medium competition.
Highly domain-specific LLM, agentic AI, and GCP expertise makes background fit sensitivity high.
Explicit 5–7 years and mandatory LLM, LangChain, MCP, Vertex AI, and GCP skills imply high strictness.
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Design, develop, deploy, and optimize scalable, secure, cloud-native AI solutions on Google Cloud Platform using Generative AI, LLM integrations, and agentic AI workflows.
Build and integrate multi-step AI workflows, including RAG pipelines, AI agents, and prompt engineering strategies leveraging LangChain, Google ADK, MCP, and vector databases.
Implement AI governance, security, responsible AI practices, and AI Guardrails; collaborate with cross-functional teams to translate requirements into enterprise-grade AI applications.
5–7 years of professional experience in software engineering, AI/ML engineering, or related field.
Strong hands-on experience with Generative AI, LLM APIs, agentic AI workflows, LangChain, Google ADK, and Model Context Protocol (MCP).
Proficiency in Python and software engineering fundamentals with experience in Google Cloud Platform services like Vertex AI, BigQuery, Cloud Storage, Cloud Functions, and Cloud Run.
Bachelor’s or Master’s degree in Computer Science, IT, AI, ML, Engineering, or related technical discipline.
Experienced in delivering production-grade Generative AI and ML solutions within enterprise-scale, cloud-native environments using GCP.
Skilled in designing automated, multi-step AI workflows and orchestration frameworks for autonomous reasoning and decision-making.
Competent in implementing AI governance, security, responsible AI, and compliance policies within AI applications and familiar with AI Guardrails development.