AI Engineer

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

Job summary

Elevait Solutions LLC in Palo Alto seeks an Applied AI Engineer to design and scale agentic AI systems. You will work on tool orchestration, memory, MCP integrations, and human-in-the-loop processes, building robust AI workflows and guardrails.

Collaboration with product, security, and platform teams will drive production-ready solutions. You will prototype new AI tools, run automated evals, and deliver pilots and PoCs across engineering groups while ensuring secure, auditable data and scalable

Qualifications

• 4+ years of professional software engineering.
• 1+ year building LLM-powered apps or agents.
• Strong Python skills.
• Experience with REST/GraphQL, OAuth2, RBAC.

Responsibilities

• 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 RBAC.
• Partner with product, security, platform, and business teams to productionize AI use cases.

Skills

Python for AI

Agentic AI

Backend APIs

Tools

Docker

Kubernetes

Job description

Job Title: Applied AI Engineer
Industry: Technology
Location: Palo Alto, CA

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
• Strong Python skills
• 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