Senior AI Platform Engineer

Job Summary

Senior Software Engineer on the AI Platform team, you will design, build, and operate the core infrastructure that powers AI capabilities across Unbxds products. You ll work on large-scale backend systems while exploring and productionizing the latest advancements in Generative AI, Agentic AI, vector search, and multimodal retrieval.

Responsibilities

• Designing and building scalable AI platform services and backend systems.
• Developing agentic workflows using modern LLM frameworks.
• Developing services around embedding models, reranking models, and multimodal search.
• Exposing AI models and algorithms as highly available internal services (ISVCs/APIs).
• Designing systems for model lifecycle management, inference, experimentation, and evaluation.
• Building orchestration frameworks for AI workflows and long-running jobs.
• Improving observability, reliability, scalability, and performance of AI infrastructure.
• Collaborating closely with Search, Product, Infrastructure, and Engineering teams to ship production-ready AI features.
• Driving architecture discussions and owning services end-to-end.

Requirements

• Must Have 5+ years of experience building large-scale backend or platform systems.
• Strong proficiency in one or more programming languages: Java/ Python/ Golang
• Strong understanding of: Distributed Systems
• System Design
• Scalability
• Performance Optimization
• Concurrency
• Experience designing and operating microservices in production.
• Hands-onexperience with containerized deployments using: Docker
• Kubernetes
• Experiencewith asynchronous systems such as: Kafka RabbitMQ
• Event-driven architectures
• Experience building REST/gRPC APIs and production backend services.
• Strong understanding of cloud-native architectures.
• Strong debugging, analytical, and problem-solving skills.
• Ability to independently own services and drive projects from design through production.

AI Platform Experience

• LLM orchestration
• Agentic workflows
• Prompt engineering
• Embedding models
• Vector databases
• Semantic search
• Model evaluation
• AI workflow orchestration
• Model serving
• Practical experience building production AI systems is preferred over academic knowledge.

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