AI Engineer

Summary:

The Enterprise AI team is Company&rsquos internal AI enablement engine. We evaluate where AI can make a real difference, build the platforms and patterns that make adoption easy, and help engineering teams across the organization work smarter and faster. We operate at the intersection of applied AI, distributed systems, and enterprise operations — our job is to make Company more efficient, one AI-powered workflow at a time. This is a high-impact, high-autonomy team where you'll work closely with engineering, product, and operations teams across Company to bring AI capabilities to life.

Responsibilities:

• Think AI-first — assess where agentic approaches genuinely outperform conventional solutions, then own the quality bar: build automated evaluations, simulation tests, and regression frameworks that keep our AI systems reliable and improving as they scale.
• Design agentic systems — tool orchestration, agent reasoning, memory, MCP integrations, and human-in-the-loop workflows.
• Define and implement AI governance patterns — guardrails, data lineage, auditability, and responsible AI practices that ensure our agentic systems are safe, compliant, and trustworthy.
• Drive adoption through pilots, proofs-of-concept, and scalable implementations across engineering teams.
• Collaborate with product, security, and platform teams to translate AI use cases into production-grade, end-to-end solutions.

Requirements:

• 5 years of professional software engineering experience, with at least 2 years focused on applied AI in production systems.

Required Skills:

• Proficient in Python and/or Go comfortable reading and writing in the other.
• Proven experience building and scaling multi-agent or agent-driven systems in production.
• Hands-on experience with modern agent ecosystems, including frameworks (e.g., LangGraph, Google ADK, Mastra, Claude Agent SDK), observability and evaluation tooling (e.g., Langfuse, LangSmith, Braintrust), MCP implementations, and leading AI SDKs (e.g., OpenAI, Anthropic).
• Strong systems and backend architecture fundamentals — designing scalable, reliable systems and handling infrastructure, performance, failure modes, cost, and deployment concerns.
• Good understanding of cloud-native environments (GCP and/or AWS) — compute, storage, networking, and managed AI services.
• Strong cross-functional collaborator and communicator, able to partner with Product, Operations, and domain experts to deliver end-to-end systems with measurable real-world impact.
• A force-multiplier on the team — you raise the bar for clarity of thinking, system design standards, and team execution.

Preferred Skills:

• Experience with AI evaluation tooling (Langfuse, LangSmith, Braintrust, or custom evaluation frameworks).
• Experience building custom MCP servers, not just consuming them.
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• AI-native builder with high velocity and ownership — intellectual curiosity, rapid adoption of new tools, bias to action, and the ability to drive ambiguous problems from concept to production.
• Hands-on experience with inference cost optimization — managing spend as agent deployments scale.

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