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# AI Developer

- **Company:** Horizontal Talent
- **Location:** Palo Alto, CA, United States
- **Experience:** Experienced
- **Salary:** $85,280.0 - $162,240.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, User Authentication, Computer Programming, Data Security, Cursor, Document Retrieval, Python (Programming Language), NoSQL, OAuth, Rapid Prototyping Process, Role-Based Access Control, OpenAI, Standard Sql, Security Assertion Markup Language (SAML), Software Engineering, Systems Architecture, Google Cloud, Cloud Platform System, GitHub Copilot, Retrieval-Augmented Generation, Claude Code, Multi-Agent Systems, Langfuse — LLM Observability and Analytics Platform, Agentic-AI, Build Management, LangSmith, Data Lineage, Graphql, Claude, Api Design, Restful APIs, Agent SDK, Human in the Loop, Docker
- **Published:** October 28, 2026
- **Apply:** https://www.dice.com/job-detail/6af7a78c-726b-4ebc-be21-c306dc5bbc67

## About the Role

* 5+ years of professional software engineering experience, including at least 2 years building applied AI solutions in production.
 * Strong programming experience in Python and/or Go, with the ability to work in both.
 * Hands-on experience building multi-agent or agent-driven systems with real operational ownership.
 * Familiarity with modern agent frameworks and AI SDKs such as LangGraph, Google ADK, Mastra, Claude Agent SDK, OpenAI, or Anthropic tools.
 * Experience with observability and evaluation tools such as Langfuse, LangSmith, Braintrust, or similar frameworks.
 * Solid backend engineering and systems architecture knowledge, including reliability, failure modes, infrastructure, and deployment considerations.
 * Experience working with cloud environments such as Google Cloud Platform and/or AWS.
 * Knowledge of enterprise API design and integration, including REST, GraphQL, authentication, authorization, OAuth2, SAML, API keys, and RBAC.
 * Comfort working with SQL and NoSQL databases, data access layers, and secure data handling patterns.
 * Strong communication and collaboration skills with a proven ability to work across functions and deliver measurable outcomes.

Preferred Skills

 * Experience building custom MCP servers.
 * Familiarity with Docker and Kubernetes.
 * Experience with inference cost optimization and managing AI spend as usage grows.
 * Hands-on use of AI-powered coding tools such as Claude Code, GitHub Copilot, Cursor, or Windsurf.
 * Experience designing RAG solutions, including vector databases, embeddings, chunking strategies, hybrid search, and document retrieval pipelines.
 * Interest in rapid prototyping, experimentation, and bringing ambiguous ideas into production.

Horizontal is committed to fostering an inclusive, respectful, and equitable workplace where different perspectives are valued and collaboration thrives. We welcome candidates of all backgrounds who are excited to contribute, learn, and help build technology that makes a meaningful impact.

## Description

Join a high-impact Enterprise AI team focused on turning emerging AI capabilities into practical, production-ready solutions that help teams work smarter and faster. This role is ideal for an experienced applied AI engineer who enjoys building agentic systems, improving reliability through rigorous evaluation, and driving adoption across a modern engineering organization.
Responsibilities

 * Identify high-value use cases where AI and agent-based approaches can create meaningful business impact.
 * Design and build agentic workflows, including tool use, orchestration, memory, human-in-the-loop patterns, and integrations with MCP.
 * Create evaluation frameworks, simulation tests, and regression checks to measure quality, reliability, and ongoing system performance.
 * Develop and ship AI-enabled tools, platforms, and proofs of concept that can scale across engineering and business teams.
 * Partner with product, operations, security, platform, and domain experts to translate needs into end-to-end solutions.
 * Apply governance best practices such as guardrails, auditability, data lineage, and responsible AI patterns.
 * Support production readiness by considering scalability, reliability, performance, deployment, and cost efficiency.
 * Help advance AI adoption by sharing patterns, enabling teams, and raising the bar for system design and execution.