AI Engineer

Benefits and Culture

Join Sony Interactive Entertainment as part of the D2C Data Science team in San Diego to design, build, and operate production AI capabilities for SPOC and digital commerce. This onsite role offers a competitive salary range and a culture grounded in collaboration, practical AI engineering, and clear impact. You will partner with operations, product, data, risk, and engineering to turn real business needs into reliable, production-ready AI solutions.

• Medical insurance
• Dental insurance
• Vision insurance
• 401(k) matching
• Paid time off
• Wellness program
• Employee discounts for Sony products
• Bonus package eligibility

Responsibilities

• Build Applied AI Features: create 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 move proven capabilities into production.
• Support Agentic Workflows and Integrations: design AI workflows that employ tool calls, structured outputs, workflow state, internal APIs, and human review patterns to take useful actions while remaining 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: build 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, focusing on reliability, resilience, security, cost efficiency, and seamless 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.

Requirements

• Educational Background: Bachelor’s degree in computer science, engineering, a related technical field, or equivalent practical experience, with 2+ years of professional software engineering experience.
• Applied AI Experience: hands-on work 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.
• 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 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.
• Structured Outputs and Tool Use: experience designing prompts, schemas, tool/function calls, workflow contracts, and validation logic so AI systems 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.
• Communication Skills: exceptional ability to translate business requirements into technical tasks, collaborate across teams, and explain AI tradeoffs in clear, practical terms.

Technologies

• Python
• Java
• AWS
• OpenSearch
• Pinecone
• Weaviate
• Redis
• pgvector
• Azure AI Search
• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• OpenAI Agents SDK
• N8N
• AWS Bedrock
• LangSmith
• Arize Phoenix
• OpenTelemetry
• Datadog
• Splunk
• New Relic
• CloudWatch

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.
• Multimodal AI Systems: experience with text, image, document, audio, or video models, including multimodal embeddings, OCR/document understanding, or content moderation workflows.
• Commerce or Trust Domain Experience: applying AI to fraud, payments, risk, customer support, marketplace operations, trust and safety, content operations, or digital commerce business processes.