Applied AI Engineer

📋 Description

• Build and operate the AI runtime, tool-calling capabilities, and orchestration infrastructure
• Design model selection and routing strategies, balancing performance, latency, and operating costs.
• Implement and improve retrieval-augmented generation (RAG), grounding, and context-retrieval
• Develop evaluation harnesses, golden datasets, and quality benchmarks to assess model and agent
• Establish evaluation frameworks for AI quality and implement guardrails and safety controls
• Take ownership of production AI behaviour, including reliability, latency, cost, and failure

🎯 Requirements

• Demonstrated experience shipping software powered by large language models (LLMs) or machine
• Strong understanding of AI evaluation methods and the ability to assess model and agent quality
• Ability to treat cost and latency as fundamental engineering constraints when designing and
• Clear communication skills, with the ability to explain technical decisions and trade-offs to
• A pragmatic, outcome-oriented mindset, with the ability to prioritize work based on customer needs
• Strong engineering judgment and the ability to make informed decisions about where AI agents are

🎁 Benefits

• Opportunity to join a founding AI engineering team and help shape the platform's architecture
• Meaningful ownership of production AI systems supporting mission-critical customer workflows.
• Close collaboration with experienced engineering and product professionals across multiple teams.
• Opportunity to influence AI strategy, evaluation practices, safety standards, and team development.
• Exposure to practical applications of commercial AI models, agent orchestration, retrieval systems