Senior Software Engineer AI (Contractor)

PlayStation Global•Aliso Viejo, CA

About The Position

Sony Interactive Entertainment is hiring a Senior Software Engineer AI to design, build, and operate production-quality agentic applications and AI-assisted engineering capabilities. The role partners with engineering teams to discover workflow needs, rapidly prototype ideas with users, and deliver successful capabilities as secure, observable, and maintainable production systems. The engineer will configure and extend Codex and Claude Code for team-wide use, integrate agentic development into CI/CD, build MCP server integrations, and create reusable tools for engineering workflows. The role owns the full lifecycle of these systems, from implementation and testing through deployment, monitoring, and iteration. This position also provides hands-on technical leadership through architecture discussions, code reviews, troubleshooting, mentoring, and documentation. The engineer will contribute standards and reusable patterns that help teams build effectively with agentic development tools, and will maintain an engineering knowledge base connected to agents through RAG and MCP.

Requirements

At least 6 years of professional software engineering experience, including significant hands-on work building and operating AI/LLM-powered or agentic applications.
Strong programming skills in Java, Python, Go, Rust, or C++.
Solid engineering fundamentals, including API design, testing, debugging, code review, and writing maintainable code.
Production-grade experience with agentic SDKs or harnesses, MCP, and LLMs, including tool use, orchestration, RAG, context management, structured output, and evaluation.
Experience designing MCP tool and resource interfaces and reasoning about tool selection, error handling, and reliability in multi-agent systems.
Demonstrated experience integrating AI into CI/CD and developer workflows.
Ability to work in ambiguous problem spaces, evaluate tradeoffs, and make pragmatic engineering decisions.
Strong written and verbal communication skills, with experience mentoring and leading through influence.
Ability to build and maintain an engineering knowledge base for agentic workflows and connect it to agents through RAG and MCP.

Nice To Haves

Expertise across agentic architecture, MCP integration, Codex or Claude Code configuration, prompt engineering, and context management.
Experience building multi-agent research or customer support systems, or structured data extraction pipelines.
Experience with AWS, Docker, Kubernetes, Terraform, and GitOps in support of AI or agentic infrastructure.
Familiarity with vector databases, embeddings, and AI evaluation frameworks.
Contributions to open-source projects involving AI, agentic systems, or developer tooling.
Strong systems thinking across models, agents, tools, context, APIs, data, and infrastructure, including reasoning about dependencies and tradeoffs across the stack.
Track record of adapting quickly to new AI tools, models, and frameworks.

Responsibilities

Design, build, and operate production-quality agentic applications, including multi-agent orchestration, subagent delegation, tool integration, and lifecycle hooks.
Design MCP tool and resource interfaces that provide coding agents and harnesses with safe, reliable access to backend systems and enterprise data.
Engineer prompts for reliable structured output and build evaluation frameworks to measure and improve output quality.
Manage context across long documents, multi-turn conversations, and multi-agent handoffs; design escalation and human-in-the-loop patterns.
Configure and extend Codex and Claude Code for team-wide use, including instruction hierarchies, custom commands, Agent Skills, path-specific rules, and plan mode.
Integrate agentic development into CI/CD pipelines for automated code review, test generation, and pull-request feedback, while minimizing false positives and duplicate findings.
Build and maintain project- and user-scoped MCP server integrations for engineering workflows.
Identify high-value opportunities for AI-assisted development and design reusable capabilities for multiple teams.
Partner with engineering teams to discover workflows, pain points, and opportunities.
Prototype and validate AI capabilities with users, and bring successful prototypes into secure, observable, maintainable production systems.
Own implementation, testing, deployment, monitoring, and iteration for agentic systems.
Provide hands-on technical leadership through architecture discussions, code reviews, and troubleshooting.
Mentor engineers on agentic design patterns, prompt engineering, and agent-native development practices.
Communicate tradeoffs, risks, and results to technical and non-technical stakeholders.
Contribute engineering standards, reusable patterns, and documentation for agentic development tools.
Build and maintain an engineering knowledge base for agentic workflows, including ingestion, organization, updates, and retrieval; connect it to agents through RAG and MCP so they can use trusted, current information with source citations.