Who we are:

The Direct to Consumer (D2C) Data Science organisation comprises Data Science, Data Engineering and ML Engineering practices. D2C Data Science helps PlayStation 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.

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:

• 3+ years (or equivalent) of Software Engineering experience within a professional working environment
• 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 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 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.

Desirable:

• Degree and/or qualification in Computer Science, Software Engineering, or a related technical field
• 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: Experience applying AI to fraud, payments, risk, customer support, marketplace operations, trust and safety, content operations, or digital commerce business processes.