Senior AI Engineer

Unity Unity · Enterprise · Tel Aviv, Israel · Engineering

Senior AI Engineer at Unity to build and deploy LLM-powered agents and production AI infrastructure, embedding agentic workflows into the engineering culture. Responsibilities include designing autonomous agents, tackling complex engineering challenges, scaling AI-driven tooling, owning the full lifecycle of AI features, building evaluation pipelines and observability tooling, and collaborating cross-functionally. Requires 5+ years of software engineering experience with 1-2 years in shipping AI/LLM systems, strong backend skills, hands-on experience with agentic systems and production LLMs, cloud platforms, and evaluation/observability tooling.

What you'd actually do

  1. Build Autonomous Agents: Design and develop autonomous agents that accelerate Aura's engineering lifecycle — from AI powered grooming, to coding, testing and shipping to production. Reduce cycle time end to end.
  2. Tackle complex engineering challenges: Contribute to the evolution of our AI capabilities. Build full-stack products and platforms that teams rely on for decision making.
  3. Scale AI-Powered Operations: Build and scale AI driven tooling that reduces production downtime and cuts developer overhead in incident research and response.
  4. Production AI Ownership: Own the full lifecycle of AI features — from prototype to production deployment, monitoring, and continuous iteration.
  5. Evaluate & Improve: Build evaluation pipelines, observability tooling, and feedback loops to measure and improve AI system quality in production.

Skills

Required

  • 5+ years as a Software Engineer
  • 1–2 years hands-on building and shipping AI/LLM-powered systems to production
  • Strong backend engineering skills
  • Sound software engineering principles — APIs, testing, and clean architecture
  • Hands-on experience designing and building LLM-powered agents using modern agentic frameworks (e.g., LangChain, LangGraph, Claude/OpenAI Agents SDK)
  • Experience building MCP servers
  • Proven track record deploying agentic applications to production — managing latency, cost, reliability, and failure modes
  • Experience building evaluation frameworks and LLM observability tooling (e.g., Langfuse or similar)
  • Hands-on with cloud platforms (GCP/AWS)
  • Containerization (Docker, Kubernetes)
  • CI/CD pipelines
  • Fluency with modern AI coding tools (Cursor, Claude Code, Copilot) and agentic workflows
  • Ability to partner with Data Scientists, ML Engineers, Product Managers, and Analysts to translate requirements into production systems
  • Strong sense of ownership and urgency
  • Comfort with ambiguity
  • Ability to operate independently and move fast

Nice to have

  • AI powered grooming
  • coding
  • testing
  • shipping to production
  • incident research and response
  • monitoring
  • continuous iteration
  • feedback loops
  • AI use cases

What the JD emphasized

  • shipping AI/LLM-powered systems to production
  • Hands-on experience designing and building LLM-powered agents
  • Proven track record deploying agentic applications to production
  • Experience building evaluation frameworks and LLM observability tooling
  • Hands-on with cloud platforms (GCP/AWS), containerization (Docker, Kubernetes), and CI/CD pipelines

Other signals

  • building production AI infrastructure
  • designing LLM powered agents
  • embedding agentic workflows into the engineering culture