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Moderate competition due to broad ML/AI role and known employer, offset by specialized agent and GCP skills.
Domain-specific agent and GCP expertise limits transferability across non-ML domains.
Multiple mandatory technical requirements but low explicit seniority creates moderate shortlisting rigidity.
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Design, build, and productionize autonomous AI Agents using Google Agent Development Kit (ADK) integrated with GCP services for data engineering task automation.
Develop and maintain Model Context Protocol (MCP) servers and integrate agents with GCP components such as Cloud Storage, BigQuery, and Vertex AI ensuring secure and efficient operation.
Deploy and operate AI agents on GCP with CI/CD pipelines, containerization, Monitoring (AgentOps), and implement evaluation strategies tracking agent accuracy, latency, and reliability.
Minimum 1 year of hands-on experience building, deploying, and operating AI Agents in a production environment.
Proficiency in Python and strong experience with Google Cloud Platform services including Google Cloud Storage and BigQuery.
Solid understanding of LLM concepts such as prompt engineering, context management, and tool/function calling.
Experience with containerization and deployment on GCP (Cloud Run, Docker), RESTful API design, and Model Context Protocol (MCP) or equivalent integration frameworks.
Experienced AI Engineer with practical skills in agentic AI development integrating complex systems on GCP for autonomous workflows.
Familiar with production-grade software engineering practices including CI/CD, containerization, monitoring (AgentOps), and evaluation of LLM-based agents.
Demonstrates strong command of AI agent orchestration frameworks (e.g., ADK, Lang Graph), and knowledge of retrieval-augmented generation (RAG) or related context assembly methods.