• Understand the AI game design and ensure that the developers understand it • Review game design from technical point of view, suggest improvements which reduce technical scope and technical risks • Ensure code maintainability and manage technical debt • Define team workflows (code reviews, automated tests, ticket granularity, number and format of sync meetings, etc.) • Ensure the quality of technical designs • Assess developers’ skills and communicate expectations to them • Ensure a reasonable velocity for delivering new features • Ensure there are no bugs that interfere with playtesting the project • Serve as the point of contact on the project regarding the implementation of gameplay features. • Collaborate with producers and game designers • Assess the feasibility of the roadmap for gameplay feature development and escalate risks

Title: AI Engineer Location: United Kingdom, London Category: D2C - Global Payment, Fraud & Decision Sciences Job Description: Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. 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. Benefits: - Hybrid working - Discretionary bonus opportunity - Private Medical Insurance - Dental Scheme - London Allowance (if applicable) - 25 days holiday per year - On Site Gym - Subsidised Café - Free soft drinks - On site bar - Access to cycle garage and showers

Title: Founding AI Engineer Location: San Francisco Department: Engineering Job Description: About kos.ai Trillions of dollars are flowing into datacenters, defense, energy, and manufacturing. The finance teams processing that capital can't keep up. 800-page invoices, thousand-page contracts, landing every few weeks, often on weekends. The world can't hire specialized accountants fast enough. Media: The Information covering our launch The Role You'll own the agent loop, the model orchestration, and the eval infrastructure. Kos is processing billions of dollars of customer work. Your systems are what keep that right. You'll own how that system gets built and how it gets better. Your work lands in production at some of the largest and fastest-growing infrastructure and defense companies in America. What you'll do: - Own the agent runtime: computer use, tool orchestration, and memory loop that lets Kos learn a workflow from customer demos - Design the ensemble routing layer that picks the right model per step and keeps Kos.ai provider-agnostic - Build the evaluation harness that catches regressions on recorded customer workflows before they reach production - Take frontier agent capabilities from paper to production, with equal attention to what the research says and what runs reliably every day - Partner with the applied controller team so practitioner-caught errors turn into prompt, model, and routing improvements - Set the hiring bar and culture for Kos.ai You - Have strong fundamentals: Bachelors / Masters / PhD in CS/EE from top-tier universities, 2+ years of experience - Have shipped an agentic system, computer-use agent, or LLM-powered feature into production. Or you have an equivalent research track record. - Worked on modern LLM orchestration, tool use, eval design for open-ended tasks, and the tradeoffs between fine-tuning, prompting, and retrieval are second nature to you - Can take a problem from "interesting paper" to "runs reliably every day for paying customers" without being told how to scope it - Track agent and LLM research closely. Useful ideas turn into production experiments in your hands quickly. - Care as much about the eval harness as the model weights. Benefits - Medical, dental, vision for you and your dependents (little/no premiums, the absolute best we qualify for) - 401(k) - Visa sponsorship Team Tanuj and Mani met at Stanford as graduate researchers under Sachin Katti. Tanuj spent his career in and around the operators Kos serves: IT, datacenters, industrials, and hardware. He previously founded Spot AI, a computer vision company which scaled to over 1,000 enterprise customers. Mani's an International Olympiad medalist, earned his Engineering PhD at Stanford, & published leading research at Microsoft Research. He then built product within Microsoft Azure's Strategic Missions and Technologies group. We built Kos because the finance and operations teams behind America's re-industrialization are talented, overwhelmed, and can't hire their way out. Every CFO we spoke to described the same problem: 800-page pay apps, thousand-page contracts, work piling up on weekends, and a talent pool for inventory, utilities, capex, and construction accounting that's simply too small to meet the moment. The back office of the buildout is the bottleneck, and it's the one nobody's building for. They deserve a partner that can keep pace with their ambition.

• Join a team that is passionate about helping each other • Work on AI solutions for logistics operations