Experienced AI Software Engineer

Augury Augury · Vertical AI · Augury, Israel · R&D

Experienced AI Software Engineer to join a GenAI Infrastructure team, focusing on building production-grade GenAI capabilities and the underlying infrastructure. The role involves hands-on engineering for AI agents, RAG pipelines, tool-calling workflows, backend services, data access, evaluation, and observability, with a strong emphasis on shipping and operating reliable AI-powered systems in production.

What you'd actually do

  1. Build reusable infrastructure, SDKs, and internal frameworks for AI-powered product capabilities.
  2. Design and implement AI agents, RAG flows, tool-calling workflows, and LLM orchestration pipelines.
  3. Build production-grade GenAI services, APIs, and backend infrastructure.
  4. Integrate LLM workflows with internal microservices, data platforms, vector search, and event-driven systems.
  5. Implement evaluation, tracing, monitoring, and quality-control mechanisms for GenAI systems.

Skills

Required

  • 6+ years of professional software engineering experience.
  • Strong Python development skills.
  • Strong backend engineering background, including APIs, services, integrations, or microservices.
  • Proven experience designing, shipping, or operating LLM-powered applications or GenAI systems.
  • Experience with RAG, AI agents, tool/function calling, prompt orchestration, evaluation, and observability.
  • Experience with microservices, distributed systems, and production backend architecture.
  • Strong understanding of system design, reliability, security, scalability, latency, and maintainability.
  • Ability to work with complex data models and expose them safely through AI systems.
  • Ability to operate in ambiguous technical areas and turn prototypes into production-ready systems.
  • Strong communication skills and ability to explain technical decisions clearly.

Nice to have

  • LangChain, LangGraph, or LangSmith.
  • Go, MongoDB, Databricks, Kubernetes, Docker, REST APIs, vector search, or event-driven systems.
  • Azure OpenAI, Gemini, or similar LLM platforms.
  • Knowledge graphs, GraphRAG, or graph databases such as Neo4j.
  • Building multi-agent systems or orchestrating multiple specialized agents/tools in production.
  • Building internal platforms, SDKs, developer tools, or shared engineering infrastructure.
  • Authorization, data segregation, multi-tenant systems, or user-level data scoping.
  • Industrial, IoT, predictive maintenance, manufacturing, or operational-data domains.

What the JD emphasized

  • Proven experience designing, shipping, or operating LLM-powered applications or GenAI systems.
  • Experience with RAG, AI agents, tool/function calling, prompt orchestration, evaluation, and observability.
  • Experience with microservices, distributed systems, and production backend architecture.
  • Ability to operate in ambiguous technical areas and turn prototypes into production-ready systems.

Other signals

  • GenAI Infrastructure
  • AI Agents
  • RAG pipelines
  • Tool-calling workflows
  • Production-grade GenAI capabilities