AI Engineer

PlayStation Global•San Diego, CA

37d•Hybrid

About The Position

Sony Interactive Entertainment (SIE) is seeking an AI Software Engineer to join the D2C ML Engineering team within the D2C Data Science organization. This role focuses on designing, building, and supporting production AI capabilities to solve high-value business problems across SPOC and the broader digital commerce ecosystem. The position is an applied AI engineering role, concentrating on transforming AI into dependable products, services, and automation, rather than model training or predictive platform ownership. The engineer will collaborate with core engineering, operations, data, risk, and product partners to build AI-powered workflows that enhance speed, quality, insight, and decision support. Potential work areas include LLM-powered services, retrieval-augmented generation (RAG), agentic workflows, tool/function calling, evaluation harnesses, guardrails, and reusable AI platform components. The ideal candidate will be a strong software engineer with backend or platform experience and practical applied AI experience, comfortable with foundation model APIs, vector and hybrid search, prompt and model evaluation, AI observability, and cloud-based services, with a willingness to learn and contribute to reliable production systems.

Requirements

Bachelor's degree in computer science, engineering, a related technical field, or equivalent practical experience.
2+ years of professional software engineering experience.
Hands-on experience building AI or generative AI features that connect model APIs to business workflows, data, documents, or internal services.
Strong software engineering skills in Python and/or Java, including API development, testing, debugging, asynchronous processing, and maintainable service design.
Experience with AWS or equivalent cloud services.
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.
Experience with AI orchestration patterns and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, OpenAI Agents SDK, N8N, AWS Bedrock Agents and Knowledge Bases, or comparable tools.
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.
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.
Exceptional communication skills, able to turn business requirements into technical tasks, collaborate across teams, and explain AI tradeoffs in clear, practical terms.

Nice To Haves

Familiarity with Model Context Protocol (MCP) or similar patterns for connecting AI applications to enterprise tools, databases, documents, and workflows.
Experience with text, image, document, audio, or video models, including multimodal embeddings, OCR/document understanding, or content moderation workflows.
Experience applying AI to fraud, payments, risk, customer support, marketplace operations, trust and safety, content operations, or digital commerce business processes.

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.