We are seeking a Senior AI Engineer to join our AI team. This is a greenfield opportunity to shape how we build and deploy intelligent systems at Aura, designing LLM powered agents, building production AI infrastructure, and embedding agentic workflows into the engineering culture. The ideal candidate is a builder at heart: someone who ships fast, operates with urgency, and sees AI not just as a tool but as a platform for rethinking how software gets built.
What you'll be doing:
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.
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.
Scale AI-Powered Operations: Build and scale AI driven tooling that reduces production downtime and cuts developer overhead in incident research and response.
Production AI Ownership: Own the full lifecycle of AI features – from prototype to production deployment, monitoring, and continuous iteration.
Evaluate \\& Improve: Build evaluation pipelines, observability tooling, and feedback loops to measure and improve AI system quality in production.
Cross-Functional Collaboration: Partner with Data Scientists, Product, and Engineering teams to identify high-value AI use cases and ship them end to end.
What you'll be doing:
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.
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.
Scale AI-Powered Operations: Build and scale AI driven tooling that reduces production downtime and cuts developer overhead in incident research and response.
Production AI Ownership: Own the full lifecycle of AI features – from prototype to production deployment, monitoring, and continuous iteration.
Evaluate \\& Improve: Build evaluation pipelines, observability tooling, and feedback loops to measure and improve AI system quality in production.
Cross-Functional Collaboration: Partner with Data Scientists, Product, and Engineering teams to identify high-value AI use cases and ship them end to end.
Requirements:
Experience: 5+ years as a Software Engineer, with 1-2 years hands-on building and shipping AI/LLM-powered systems to production.
Engineering Fundamentals: Strong backend engineering skills with sound software engineering principles – APIs, testing, and clean architecture.
Agentic Systems: Hands-on experience designing and building LLM-powered agents using modern agentic frameworks (e.g., LangChain, LangGraph, Claude/OpenAI Agents SDK), including experience building MCP servers.
Production LLM: Proven track record deploying agentic applications to production – managing latency, cost, reliability, and failure modes.
Evaluation \\& Observability: Experience building evaluation frameworks and LLM observability tooling (e.g., Langfuse or similar).
Cloud \\& Infrastructure: Hands-on with cloud platforms (GCP/AWS), containerization (Docker, Kubernetes), and CI/CD pipelines.
AI Tooling: Fluency with modern AI coding tools (Cursor, Claude Code, Copilot) and agentic workflows – leveraging AI to accelerate the full software development lifecycle.
Cross-Functional Collaboration: Ability to partner with Data Scientists, ML Engineers, Product Managers, and Analysts to translate requirements into production systems.
Ownership \\& Urgency: Strong sense of ownership and urgency, comfort with ambiguity, ability to operate independently and move fast.
Experience: 5+ years as a Software Engineer, with 1-2 years hands-on building and shipping AI/LLM-powered systems to production.
Engineering Fundamentals: Strong backend engineering skills with sound software engineering principles – APIs, testing, and clean architecture.
Agentic Systems: Hands-on experience designing and building LLM-powered agents using modern agentic frameworks (e.g., LangChain, LangGraph, Claude/OpenAI Agents SDK), including experience building MCP servers.
Production LLM: Proven track record deploying agentic applications to production – managing latency, cost, reliability, and failure modes.
Evaluation \\& Observability: Experience building evaluation frameworks and LLM observability tooling (e.g., Langfuse or similar).
Cloud \\& Infrastructure: Hands-on with cloud platforms (GCP/AWS), containerization (Docker, Kubernetes), and CI/CD pipelines.
AI Tooling: Fluency with modern AI coding tools (Cursor, Claude Code, Copilot) and agentic workflows – leveraging AI to accelerate the full software development lifecycle.
Cross-Functional Collaboration: Ability to partner with Data Scientists, ML Engineers, Product Managers, and Analysts to translate requirements into production systems.
Ownership \\& Urgency: Strong sense of ownership and urgency, comfort with ambiguity, ability to operate independently and move fast.
This position is open to all candidates.
