Quick Overview
Job Description
Title: Software Engineer
Job Title: AI Engineer (Agent Development Kit / ADK)
Experience Level: [3+ years AI Engineer, 8+ as Software Engineer]
About the Role We're looking for an AI Engineer to design and build production-grade AI agents using Google's Agent Development Kit (ADK).
You'll work on multi-agent systems that integrate with enterprise data platforms and business tools, taking agents from prototype through to reliable production deployment.
What You'll Work On -
Designing and building AI agents and multi-agent workflows using ADK -
Agent orchestration patterns: tool use, function calling, sub-agent delegation, and state/session management -
Integrating agents with enterprise data sources (Snowflake, Salesforce, or similar) and internal APIs -
Deploying and scaling agents on Google Cloud (Vertex AI Agent Engine or equivalent)
- Evaluating and tuning agent performance, latency, and cost across different model backends -
Collaborating with data engineering and platform teams to expose reliable, well-governed data to agents Must Have -
Hands-on experience building agents with Google ADK (or comparable agent frameworks such as LangGraph, AutoGen, CrewAI, with strong ability to ramp on ADK)
- Strong Python development skills - Experience with LLM APIs (Gemini, Claude, OpenAI, or similar) and prompt/tool design -
Experience with REST APIs and backend service integration -
Familiarity with Google Cloud Platform (Vertex AI, Cloud Run, IAM, Secrets Manager) Good to Have -
Experience with Model Context Protocol (MCP) or other agent-to-tool integration standards -
SQL proficiency, ideally with Snowflake (CTEs, Dynamic Tables, Views)
- Experience with Snowflake Cortex / Cortex Analyst -
- Experience with evaluation frameworks for LLM/agent output quality -
8+ years of software engineering experience, with recent focus on applied AI/agent systems -
Strong Python skills; TypeScript/JavaScript a plus -
Experience shipping AI features to production, not just prototypes -
Comfortable working independently in ambiguous, fast-evolving technical territory -
Clear communicator, able to explain agent behavior and architecture to both technical and non-technical stakeholders What Success Looks Like -
Agents that are reliable, observable, and behave predictably in production -
Clean integration between agents and underlying enterprise data/systems -
Reduced manual effort for end users through well-scoped agent automation
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