This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
We are looking for a Software Engineer to design and implement core platform capabilities for AI/ML and AI Agents in SingleStore Cloud. You’ll work on services that enable model/tool orchestration (e.g. MCP style tool discovery and execution), agent workflows, retrieval pipelines (embeddings/vector search), evaluation/observability, and secure multi tenant operations. You will likely find yourself using Go and Python, Kubernetes, cloud primitives, and the right tools for the job, while applying solid AI/ML fundamentals to make correct engineering decisions.
Job Responsibility:
Build and evolve backend services that power AI features: agent orchestration, tool execution, retrieval/RAG pipelines, and model serving integrations
Design APIs and control plane workflows for AI platform components (tenant-aware, secure by default, observable)
Implement MCP style tool discovery / integration patterns so agents can safely call tools, connectors, and internal services
Work closely with product managers, designers, customers, and partner engineering teams to deliver high quality AI experiences
Engineer for reliability and scale: latency, cost controls, rate limiting, fallbacks, rollouts, and incident response readiness
Establish best practices around evaluation: offline test sets, regression detection, prompt/model/version tracking, and quality gates
Contribute to secure AI by design approaches: permissions, data access boundaries, prompt injection defenses, and auditability
Mentor junior engineers and contribute to a welcoming, high ownership team environment
Requirements:
Strong software engineering skills with experience in distributed systems (Go, Python, or similar)
Experience building cloud native services: Kubernetes, containers, service-to-service APIs, CI/CD
4+ years of experience working on a SaaS product or production platform
Solid understanding of AI/ML fundamentals: Supervised learning basics (training vs inference, overfitting, evaluation metrics, classification, anomaly detection, forecasting, regression etc.)