AI Engineer

Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the Play Station brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting‑edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world.

Our role at SIE is to create and nurture the experiences under the Play Station brand, a name synonymous with entertainment excellence and creativity.

Who we are

The Direct to Consumer (D2C) Data Science organization comprises Data Science, Data Engineering and ML Engineering practices. D2C Data Science helps Play Station grow and operate its digital business across commerce, subscriptions, payments, lifecycle experiences and player‑facing services. We partner with product, engineering, finance, marketing and operations teams to turn experimentation, forecasting, AI and production‑quality measurement into better player experiences.

Role overview

Within D2C Data Science, the D2C ML Engineering team is seeking an AI Software Engineer to help design, build, and support production AI capabilities that solve high‑value business problems across SPOC and the broader digital commerce ecosystem. This is not a model‑training or predictive‑platform ownership role; it is an applied AI engineering role focused on turning AI into dependable products, services, and automation.

You will work with core engineering, operations, data, risk, and product partners to build AI‑powered workflows that improve speed, quality, insight, and decision support. The work may include LLM‑powered services, retrieval‑augmented generation (RAG), agentic workflows, tool/function calling, evaluation harnesses, guardrails, and reusable AI platform components.

The ideal candidate is a strong software engineer with backend or platform experience and practical hands‑on applied AI experience. You are comfortable building with foundation model APIs, vector and hybrid search, prompt and model evaluation, AI observability, and cloud‑based services, and you are eager to learn while contributing to reliable production systems.

Build Applied AI Features: Implement services, workflows, and reusable components for LLM‑powered automation, retrieval, tool use, summarization, classification, decision support, and knowledge workflows.

Solve Business Problems with AI: Collaborate with operations, product, data, risk, and engineering stakeholders to understand use cases, prototype solutions, measure outcomes, and help move proven capabilities into production.

Support Agentic Workflows and Integrations: Build AI workflows that use tool/function calling, structured outputs, workflow state, internal APIs, and human review patterns to take useful action while staying auditable and controlled.

Develop Retrieval and Knowledge Systems: Contribute to RAG and agentic retrieval pipelines over enterprise content and operational data using embeddings, vector databases, hybrid search, reranking, citations, access controls, and freshness strategies.

Improve AI Quality, Safety and Evaluation: Create and maintain evaluation suites, regression tests, prompt/model versioning, trace analysis, guardrails, policy checks, PII handling, hallucination mitigation, and operational monitoring.

Production AI Engineering: Develop scalable APIs, microservices, and event‑driven workflows in Python or Java, with attention to reliability, resilience, security, cost efficiency, and clean integration with existing services.

Cloud Delivery and Automation: Deploy AI services using AWS, containers, infrastructure as code, CI/CD pipelines, secrets management, observability, and operational runbooks.

Cross‑Functional

Collaboration:

Participate in design reviews, implementation planning, troubleshooting, documentation, and knowledge sharing across technical and non‑technical teams.

Educational Background: …