Artificial Intelligence Engineer

CloudFulcrum, LLC; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

CloudFulcrum, LLC seeks a seasoned software engineer to advance agentic AI systems in production. You will design orchestration, memory, and MCP integrations while upholding governance and safety patterns. The role emphasizes scalable evaluation, testing, and cross-team collaboration.

You will partner with product, security, and platform teams to translate AI use cases into robust, end-to-end solutions that scale in modern cloud environments.

Qualifications

• 5+ years of professional software engineering with at least 2 years focused on applied AI in production systems.
• 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 and eval tooling.
• Strong systems and backend architecture fundamentals.
• Good understanding of cloud-native environments (GCP and/or AWS).
• Experience designing and integrating with enterprise APIs (REST, GraphQL).
• Comfortable with backend databases and RBAC patterns.
• Strong cross-functional collaborator and communicator.

Responsibilities

• Think AI-first — assess where agentic approaches genuinely outperform conventional solutions, then own the quality bar with automated evals, simulation tests, and regression frameworks.
• 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.
• 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 end-to-end production-grade solutions.

Skills

Python

Go

Distributed systems

Multi-agent systems

Production AI

API design

Cloud-native

Observability tooling

LangGraph

LangSmith

OpenAI / Anthropic SDKs

OAuth2 / SAML / RBAC

Tools

LangGraph

Google ADK

Mastra

Claude Agent SDK

Langfuse

LangSmith

Braintrust

OpenAI

Anthropic

Docker

Kubernetes

Job description

Think AI-first — assess where agentic approaches genuinely outperform conventional solutions, then own the quality bar: build automated evals, 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 various business functions, product, security, and platform teams to translate AI use cases into production-grade, end-to-end solutions.

What We're Looking For

• 5+ years of professional software engineering, with at least 2 years focused on applied AI in production systems.
• 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 — real-world operational ownership, not just simple LLM workflows.
• Hands-on experience with modern agent ecosystems, including frameworks (e.g., LangGraph, Google ADK, Mastra, Claude Agent SDK), observability and evals 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.
• Experience designing and integrating with enterprise APIs (REST, GraphQL) including authentication and authorization patterns (OAuth2, SAML, API keys, RBAC). Comfortable working with backend databases (SQL and NoSQL) — writing queries, understanding data models, and building data access layers that enforce role-based access control.
• 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.

Nice to Have

• Experience with AI evaluation tooling (Langfuse, LangSmith, Braintrust, or custom eval 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.
• Experience using AI-powered coding agents (e.g., Claude Code, GitHub Copilot, Cursor, Windsurf) to accelerate development workflows — rapid prototyping, code generation, debugging, and test writing.
• Experience with RAG (Retrieval-Augmented Generation) architectures and document retrieval pipelines — vector databases, embedding models, chunking strategies, and hybrid search — for building agents that answer questions grounded in enterprise documentation.