AI Engineer

AI Software Engineer - D2C - SPOC

Sony Interactive Entertainment (SIE) Play Station;
San Diego, CA

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.

What You'll Be Doing

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.

What We're Looking For

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 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

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

Agent and Workflow Orchestration:
Experience with AI orchestration patterns and tools such as Lang Chain, Lang Graph, Llama Index, 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 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 Lang Smith, Arize Phoenix, Open Telemetry, Datadog, Splunk, New Relic, Cloud Watch, or comparable platforms.

Communication

Skills:

Possesses exceptional communication skills, able 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…