Senior Software Engineer AI (Contractor)

5 days ago

Aliso Viejo, CA, USASenior

H1B sponsor

Responsibilities

Design, build, and operate production-quality agentic applications with multi-agent orchestration, subagent delegation, tool integration, and lifecycle hooks.
Design MCP tool and resource interfaces that provide safe, reliable access to backend systems and enterprise data.
Engineer prompts, structured-output workflows, evaluation frameworks, context-management strategies, and human-in-the-loop escalation patterns.
Configure Codex and Claude Code for team-wide use and integrate agentic development into developer workflows and CI/CD.
Build and maintain MCP server integrations and reusable AI-assisted development capabilities across engineering teams.
Prototype AI capabilities with users and own their implementation, testing, deployment, monitoring, and iteration in production.
Provide hands-on technical leadership through architecture discussions, code reviews, troubleshooting, mentoring, and documentation.
Build and maintain an engineering knowledge base connected to agents through RAG and MCP with trusted, current information and source citations.

6+ years of professional software engineering experience, including significant hands-on experience building and operating AI-, LLM-, or agentic applications.
Strong programming skills in one or more of Java, Python, Go, Rust, or C++, with solid API design, testing, debugging, code review, and maintainability fundamentals.
Production-grade experience with agentic SDKs or harnesses, MCP, LLMs, tool use, orchestration, RAG, context management, structured output, and evaluation.
Experience designing MCP tool and resource interfaces and addressing tool selection, error handling, and reliability in multi-agent systems.
Experience integrating AI into CI/CD and developer workflows.
Ability to make pragmatic engineering decisions in ambiguous problem spaces and communicate tradeoffs, risks, and results.
Experience mentoring and leading through influence.
Experience building knowledge bases with content ingestion, organization, updates, retrieval, RAG, MCP, and source citations.
Preferred experience includes multi-agent research or customer support systems, structured data extraction pipelines, AWS, Docker, Kubernetes, Terraform, GitOps, vector databases, embeddings, AI evaluation frameworks, and relevant open-source contributions.
Strong systems thinking and the ability to adapt quickly to new AI tools, models, and frameworks are preferred.