Senior Software Engineer AI (Contractor)

PlayStation•Aliso Viejo, CA

About The Position

Sony Interactive Entertainment (SIE), the company behind the PlayStation brand, is a technology and entertainment company delivering hardware, network services, and well-known intellectual properties. The Senior Software Engineer AI will design, build, and operate production-quality agentic applications and developer tools, partnering with engineering teams to identify opportunities and deliver secure, observable, maintainable AI capabilities. The role covers agentic architecture and engineering, developer productivity, discovery and prototyping, production delivery, and technical leadership. It includes configuring and extending Codex and Claude Code, integrating agents into engineering workflows and CI/CD, building MCP integrations, and developing an engineering knowledge base that agents can access through RAG and MCP. The engineer will own solutions through implementation, testing, deployment, monitoring, and iteration, while mentoring colleagues and contributing reusable standards and documentation. Sony Interactive Entertainment conducts background checks at the offer stage for new employees; some roles may include criminal background checks. The company is an Equal Opportunity Employer and a Fair Chance employer. Qualified applicants with arrest and conviction records will be considered.

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 maintainable code.
Production-grade practical 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.
Experience building and maintaining an engineering knowledge base for agentic workflows, and connecting it to agents using RAG and MCP.

Nice To Haves

Demonstrated 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 and ability to reason about how models, agents, tools, context, APIs, data, and infrastructure fit together as production systems, including 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 using agentic SDKs and harnesses, 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 for production reliability.
Configure and extend Codex and Claude Code for team-wide use, including instruction hierarchies, custom slash commands, Agent Skills, path-specific rules, and plan mode.
Integrate agentic development into CI/CD pipelines for automated code review, test generation, and PR feedback, tuning systems to minimize false positives and duplicate findings.
Build and maintain project- and user-scoped MCP server integrations for engineering workflows.
Identify high-value AI-assisted development opportunities and design reusable capabilities for multiple teams.
Partner with engineering teams to discover workflows, pain points, and opportunities through hands-on collaboration.
Prototype and validate AI capabilities with users, then bring successful prototypes into secure, observable, maintainable production systems.
Own implementation, testing, deployment, monitoring, and iteration across the solution lifecycle.
Provide technical leadership through architecture discussions, code reviews, and troubleshooting of agentic systems.
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 content ingestion, organization, updates, and retrieval.
Connect the knowledge base to agents through RAG and MCP so they can use trusted, current information with source citations.