E
AI Engineer
Elevait Solutions
4 hours ago
Full-time
On-site
Palo Alto, California, United States
Client Overview
Our client is a publicly traded data security software company headquartered in Silicon Valley, serving enterprises worldwide with backup, recovery, and cyber resilience solutions. Top 3 Skills
Python development for LLM-powered applications and AI agents Agentic AI systems, including tool orchestration, MCP integrations, and automated LLM evaluations Cloud-native backend and API development (REST, GraphQL, OAuth2, RBAC) What You ll Do
Assess where agentic approaches outperform conventional solutions for internal workflows Design and build agentic systems covering tool orchestration, agent reasoning, memory, MCP integrations, and human-in-the-loop steps Build automated evals, simulation tests, and regression frameworks to keep AI systems reliable as they scale Implement AI guardrails, data lineage, auditability, and responsible AI practices Prototype and evaluate emerging AI tools and technologies Deliver pilots, proofs of concept, and scalable implementations across engineering teams Integrate agents with enterprise APIs and SQL/NoSQL databases using role-based access controls Partner with product, security, platform, and business teams to take AI use cases to production What We Need From You
4+ years of professional software engineering, including 1+ year building LLM-powered applications or agents Hands-on experience building agentic workflows with tool calling and a leading LLM SDK such as OpenAI or Anthropic Backend fundamentals, including REST or GraphQL APIs, authentication and authorization patterns (OAuth2, SAML, RBAC), and SQL/NoSQL databases Working knowledge of a cloud platform (AWS or GCP), including compute, storage, and managed AI services Preferred / Bonus
Go (Golang) experience Production ownership of multi-agent systems at scale Agent frameworks such as LangGraph, Google ADK, Mastra, or the Claude Agent SDK Evaluation and observability tooling such as Langfuse, LangSmith, or Braintrust Building custom MCP servers Docker and Kubernetes Inference cost optimization for scaling agent deployments AI coding agents such as Claude Code, GitHub Copilot, Cursor, or Windsurf RAG architectures, including vector databases, embedding models, chunking, and hybrid search
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Our client is a publicly traded data security software company headquartered in Silicon Valley, serving enterprises worldwide with backup, recovery, and cyber resilience solutions. Top 3 Skills
Python development for LLM-powered applications and AI agents Agentic AI systems, including tool orchestration, MCP integrations, and automated LLM evaluations Cloud-native backend and API development (REST, GraphQL, OAuth2, RBAC) What You ll Do
Assess where agentic approaches outperform conventional solutions for internal workflows Design and build agentic systems covering tool orchestration, agent reasoning, memory, MCP integrations, and human-in-the-loop steps Build automated evals, simulation tests, and regression frameworks to keep AI systems reliable as they scale Implement AI guardrails, data lineage, auditability, and responsible AI practices Prototype and evaluate emerging AI tools and technologies Deliver pilots, proofs of concept, and scalable implementations across engineering teams Integrate agents with enterprise APIs and SQL/NoSQL databases using role-based access controls Partner with product, security, platform, and business teams to take AI use cases to production What We Need From You
4+ years of professional software engineering, including 1+ year building LLM-powered applications or agents Hands-on experience building agentic workflows with tool calling and a leading LLM SDK such as OpenAI or Anthropic Backend fundamentals, including REST or GraphQL APIs, authentication and authorization patterns (OAuth2, SAML, RBAC), and SQL/NoSQL databases Working knowledge of a cloud platform (AWS or GCP), including compute, storage, and managed AI services Preferred / Bonus
Go (Golang) experience Production ownership of multi-agent systems at scale Agent frameworks such as LangGraph, Google ADK, Mastra, or the Claude Agent SDK Evaluation and observability tooling such as Langfuse, LangSmith, or Braintrust Building custom MCP servers Docker and Kubernetes Inference cost optimization for scaling agent deployments AI coding agents such as Claude Code, GitHub Copilot, Cursor, or Windsurf RAG architectures, including vector databases, embedding models, chunking, and hybrid search
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