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Metro location and mid-level experience increase competition, while niche agentic skillset reduces it.
Role requires specialized applied AI, agentic systems, and production ML experience, so cross-industry transfer is limited.
Explicit 5+ years and 2+ years applied AI plus specific agent and production requirements create strict filters.
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Evaluate and integrate agentic AI systems where they outperform traditional solutions, owning reliability through automated evaluations and regression testing.
Design and implement agentic AI architectures including tool orchestration, agent reasoning, memory, MCP integrations, and human-in-the-loop workflows.
Establish AI governance frameworks for safety, compliance, auditability, and drive enterprise-wide adoption via pilots and scalable implementations.
5+ years professional software engineering experience with at least 2 years in applied AI production systems.
Proficiency in Python and/or Go with ability to read and write in the other language.
Proven operational ownership building and scaling multi-agent or agent-driven systems in production environments.
Experience with cloud-native platforms (GCP and/or AWS), enterprise API design (REST, GraphQL), authentication/authorization patterns (OAuth2, SAML, RBAC), and backend databases (SQL and NoSQL).
Strong systems architect with focus on scalable, reliable backend AI systems considering infrastructure performance, failure modes, cost, and deployment.
Experienced in multi-agent AI ecosystems and observability tooling (LangGraph, Langfuse, Claude Agent SDK, etc.) and integrating agentic AI models responsibly at enterprise scale.
Collaborative cross-functional operator who can partner with product, ops, security, and platform teams to deliver measurable impact and raise team execution standards.