AI Engineer

AI Software Engineer - D2C - SPOC

Sony Interactive Entertainment (SIE) PlayStation; San Diego, CA

Who We Are

At Sony Interactive Entertainment (SIE) PlayStation, we create unforgettable gaming experiences and we are just as committed to building a world-class team. As a leader in interactive entertainment, PlayStation operates at a massive scale, supporting millions of players worldwide and processing billions of transactions annually across our digital ecosystem.

Our Store Publishing Operations & Commerce (SPOC) team is responsible for ensuring trust, security, and seamless transactions across PlayStation's digital store. We provide data-driven systems and AI capabilities that support platform services, customer experience, payments, fraud prevention, risk management, and business operations.

This role is part of the broader technology organization, but its focus is applied AI engineering: using modern AI systems to solve business problems and helping create reusable capabilities that allow teams to apply AI safely, reliably, and efficiently.

About the Role

We are 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 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, structured outputs, 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.

Key Responsibilities

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.

Qualifications and Education Requirements

Educational Background: Bachelor's degree in computer science, engineering, a related technical field.

Applied AI Experience: Hands-on experience building AI or generative AI features that connect model APIs to business workflows, data, documents, or internal services.

Coding Proficiency: Strong software engineering skills in Python and/or Java, including API development, testing, debugging, asynchronous processing, and maintainable service design.

Cloud Competency: Experience with AWS or equivalent cloud services such as EKS/ECS, Lambda, S3, DynamoDB, Kinesis, EventBridge, SNS, SQS, IAM, or related cloud infrastructure.

Foundation Model Platforms: Experience using model APIs and platforms such as OpenAI/Azure OpenAI, Anthropic Claude, Google Gemini or Vertex AI, AWS Bedrock, Mistral, Meta Llama, or comparable services.

RAG and Retrieval Systems: Familiarity with embeddings, chunking, indexing, retrieval strategies, vector and hybrid search, reranking, citations, and vector stores such as OpenSearch, Pinecone, Weaviate, Redis, pgvector, Azure AI Search, or similar technologies.

Agent and Workflow Orchestration: Experience or familiarity with AI orchestration patterns and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, OpenAI Agents SDK, AWS Bedrock Agents and Knowledge Bases, or comparable tools.

Structured Outputs and Tool Use: Experience designing prompts, schemas, tool/function calls, workflow contracts, and validation logic so AI systems can produce dependable outputs and interact safely with internal systems.

AI Observability and Evaluation: Familiarity with tracing, monitoring, evals, prompt testing, quality metrics, and debugging tools such as LangSmith, Arize Phoenix, OpenTelemetry, Datadog, Splunk, New Relic, CloudWatch, or comparable platforms.

Security and Governance: Understanding of enterprise data security, access control, privacy, responsible AI practices, auditability, and safe handling of sensitive commerce and customer data.

Communication Skills: Ability to turn business requirements into technical tasks, collaborate across teams, and explain AI tradeoffs in clear, practical terms.

Preferred Skills

Model Context and Connectors: Familiarity with Model Context Protocol (MCP) or similar patterns for connecting AI applications to enterprise tools, databases, documents, and workflows.

AI Platform Components: Experience contributing to model gateways, prompt registries, reusable tool libraries, workflow templates, evaluation dashboards, or other shared capabilities that help teams adopt AI consistently.

Multimodal AI Systems: Experience with text, image, document, audio, or video models, including multimodal embeddings, OCR/document understanding, or content moderation workflows.

Responsible AI and Guardrails: Exposure to safety alignment, policy enforcement, prompt-injection defenses, data loss prevention, content filtering, red teaming, and human review workflows for AI products.

Data and Event Platforms: Familiarity with Kafka, Flink, Spark, dbt, Airflow, or similar data and orchestration tools where they support AI-powered workflows and operational automation.

Commerce or Trust Domain Experience: Experience applying AI to fraud, payments, risk, customer support, marketplace operations, trust and safety, content operations, or digital commerce business processes.

PlayStation isn't just the Best Place to Play - it's also the Best Place to Work. We've thrilled gamers since 1994 when we launched the original PlayStation. Today, we're recognized as a global leader in interactive and digital entertainment. The PlayStation brand falls under Sony Interactive Entertainment, a wholly owned subsidiary of Sony Corporation.

Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to race, color, religion, gender, pregnancy, national origin, ancestry, citizenship, age, legally protected physical or mental disability, covered veteran status, status in the U.S. uniformed services, sexual orientation, marital status, genetic information or membership in any other legally protected category.

We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond.

We sincerely appreciate the time and effort you spent in contacting us and we thank you for your interest in PlayStation.