Were Hiring Senior Platform Engineer
About The Role:
As a Senior Platform Engineer, you will build the shared backend platform and AI enablement layer that every product team builds on top of, and you will be a key design partner across R&D – helping teams shape architectures for new AI-powered features, backend services, and agentic systems.
This is a force-multiplier role: instead of shipping one product feature, you raise the velocity and quality of every team. You will make key architectural decisions about how AI is integrated, how services communicate, and how engineers measure and improve what they ship – and you will coach and review designs across the org so we move fast without compromising on quality.
What You'll Do
Lead and partner on architecture and design across R&D – running design reviews, shaping technical proposals, and helping teams choose the right patterns for AI, backend, and data-driven systems
Design and build core backend platform services and SDKs (auth, eventing, feature flags, configuration, data access) that product teams compose into AI-powered features
Build the AI enablement layer: shared LLM gateways, prompt and agent frameworks, evaluation and tracing tooling, model routing, guardrails, and cost/latency controls – so every team can adopt LLMs and agents safely and consistently
Define and own platform processes that improve engineering velocity and quality: service templates, paved-road patterns, code review standards, release workflows, and golden-path documentation
Build the observability and quality story for AI features end-to-end: structured logging, metrics, distributed tracing, LLM-call instrumentation, prompt/response evaluations, and regression detection
Research, prototype, and lead the selective adoption of new AI tooling, agent frameworks, and backend technologies into the platform.
About The Role:
As a Senior Platform Engineer, you will build the shared backend platform and AI enablement layer that every product team builds on top of, and you will be a key design partner across R&D – helping teams shape architectures for new AI-powered features, backend services, and agentic systems.
This is a force-multiplier role: instead of shipping one product feature, you raise the velocity and quality of every team. You will make key architectural decisions about how AI is integrated, how services communicate, and how engineers measure and improve what they ship – and you will coach and review designs across the org so we move fast without compromising on quality.
What You'll Do
Lead and partner on architecture and design across R&D – running design reviews, shaping technical proposals, and helping teams choose the right patterns for AI, backend, and data-driven systems
Design and build core backend platform services and SDKs (auth, eventing, feature flags, configuration, data access) that product teams compose into AI-powered features
Build the AI enablement layer: shared LLM gateways, prompt and agent frameworks, evaluation and tracing tooling, model routing, guardrails, and cost/latency controls – so every team can adopt LLMs and agents safely and consistently
Define and own platform processes that improve engineering velocity and quality: service templates, paved-road patterns, code review standards, release workflows, and golden-path documentation
Build the observability and quality story for AI features end-to-end: structured logging, metrics, distributed tracing, LLM-call instrumentation, prompt/response evaluations, and regression detection
Research, prototype, and lead the selective adoption of new AI tooling, agent frameworks, and backend technologies into the platform.
Requirements:
4+ years of experience in backend / software engineering, with proven experience designing and developing high-performance, distributed systems
Strong proficiency in Python and Node.js
Proven experience working in cloud environments (AWS preferred; GCP/Azure acceptable)
Hands-on experience with microservices, containerized environments (Kubernetes), and CI/CD pipelines (GitHub Actions)
Experience with message queuing and streaming systems such as Kafka and/or SQS
Strong understanding of SQL and NoSQL databases, large-scale data flows, and data-driven systems
Experience in developing and deploying LLM agents to production (via Langgraph, Langchain, etc.)
Strong collaboration and communication skills, both Hebrew & English.
4+ years of experience in backend / software engineering, with proven experience designing and developing high-performance, distributed systems
Strong proficiency in Python and Node.js
Proven experience working in cloud environments (AWS preferred; GCP/Azure acceptable)
Hands-on experience with microservices, containerized environments (Kubernetes), and CI/CD pipelines (GitHub Actions)
Experience with message queuing and streaming systems such as Kafka and/or SQS
Strong understanding of SQL and NoSQL databases, large-scale data flows, and data-driven systems
Experience in developing and deploying LLM agents to production (via Langgraph, Langchain, etc.)
Strong collaboration and communication skills, both Hebrew & English.
This position is open to all candidates.
