Senior Software Engineer - AI & Agentic Architecture

PlayStation Global•Aliso Viejo, CA

5d•$177,300 - $265,900

About The Position

Sony Interactive Entertainment is hiring a senior individual-contributor to combine hands-on engineering with technical leadership and close collaboration across engineering, security, and product teams. The role designs and ships agentic systems, developer tooling, and reusable platform capabilities to improve productivity across SIE. The engineer will build and operate production-quality agentic applications, design safe and reliable MCP interfaces, improve prompt and output quality, and manage context and human-in-the-loop patterns. The position also extends Codex and Claude Code for team-wide use, integrates AI into CI/CD and engineering workflows, and builds MCP server integrations. The role partners with engineering teams to discover workflow needs, prototype AI capabilities with real users, and deliver successful prototypes as secure, observable, maintainable production systems. Responsibilities span implementation, testing, deployment, monitoring, iteration, technical leadership, mentoring, communication of tradeoffs, and contribution to engineering standards and documentation.

Requirements

At least 6 years of professional software engineering experience, including significant hands-on experience building and operating AI/LLM-powered or agentic applications.
Strong programming skills in Java, Python, Go, Rust, or C++, with solid engineering fundamentals in API design, testing, debugging, code review, and 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.
Bachelor’s degree or equivalent.

Nice To Haves

Expertise across agentic architecture, MCP integration, Codex/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 dependencies and tradeoffs across models, agents, tools, context, APIs, data, and infrastructure.
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.
Configure and extend Codex and Claude Code for team-wide use, including instruction-file 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 pull-request feedback, 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 create reusable capabilities for multiple teams.
Partner with engineering teams to discover workflows, pain points, and opportunities through hands-on engagement.
Prototype and validate AI capabilities with users, then deliver successful prototypes as secure, observable, maintainable production systems.
Own implementation, testing, deployment, monitoring, and iteration across the full lifecycle.
Provide hands-on 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.