Senior ML/AI Software Engineer, PlayStation

• The ideal candidate has hands-on experience building scalable, automated systems
• You combine backend engineering expertise with applied AI knowledge, using technologies like LangChain, vector databases, and LLM APIs alongside microservices, Kubernetes, Terraform, and CI/CD pipelines to create resilient, intelligent systems
• Educational Background: Bachelor’s degree with 6+ years of experience in machine learning, backend engineering, or AI platform development
• Coding Proficiency: Demonstrated experience with Java and Python
• Cloud Competency: Proficiency with common AWS services or equivalent such as EKS/ECS, Kinesis, Lambda, DynamoDB, SNS, and SQS
• Systems Monitoring and Analytics: Knowledge of systems monitoring, alerting, and analytics using tools such as Datadog, Splunk, New Relic, or AWS CloudTrail
• Communication Skills: Demonstrated success in cross-functional collaboration
• LLM Expertise: Proven experience developing, evaluating and orchestrating agentic workflows
• MLOps and Distributed Systems: Hands-on experience with distributed systems and MLOps tooling such as Kubernetes, Docker, MLflow, Airflow, Terraform, and CI/CD

Desirable

• Our Store Publishing Operations & Commerce (SPOC) team is responsible for ensuring trust, security, and seamless transactions across PlayStation’s digital store
• We provide data-driven solutions to support platform services, customer experience, payments, fraud prevention, and risk management
• By using advanced analytics and machine learning/AI, we assist in protecting the commerce ecosystem, facilitating business growth, and optimizing player experiences
• We are seeking a Senior ML/AI Engineer to lead the design, optimization, and deployment of large-scale automation efforts, utilizing LLMs and generative AI as appropriate
• This role sits at the intersection of machine learning, backend engineering, and cloud-scale infrastructure, focusing on building intelligent systems that power our teams
• You’ll partner closely with devOps, data science, and data engineering to deploy scalable, reliable, and cost-efficient automation solutions that increase efficiencies, and accelerates innovation across the organization
• Automate Workflows: Architect, build, test, and monitor AWS-based workflows to solve critical business problems
• Microservices and APIs: Develop microservices for ML-driven applications using Python or Java, ensuring scalability and resilience
• Service Availability: Guarantee high levels of service availability through participation in an on-call rotation, following best practices for disaster recovery and business continuity
• Automated Deployment: Ensure all work is deployed in an automated, repeatable fashion, optimizing infrastructure for cost and efficiency