Job Description

We’re looking for an AI Engineer to design, implement, and optimize advanced AI systems that balance quality, performance, and cost. You’ll work on inference pipelines, retrieval-augmented generation (RAG), and multi-agent patterns while building evaluation harnesses and simulation-based training frameworks. This role combines deep technical skill with a strong focus on real-world reliability and user experience.

System Design: Architect inference and RAG pipelines; design manager/worker agent patterns with deterministic fallback mechanisms for reliability.

Evaluation & Training: Build robust evaluation harnesses and develop simulation-based training pipelines to improve model robustness.

Optimization: Tune AI systems across quality, latency, and cost dimensions, ensuring scalable production performance.

Experimentation: Run A/B tests and continuous feedback loops to measure system performance and guide improvements.

User Experience: Collaborate with product and design teams to ensure AI outputs integrate into workflows with intuitive, high-quality UX.

Requirements

• Strong background in machine learning, AI systems, or applied research
• Hands-on experience with inference optimization and retrieval-augmented generation (RAG)
• Familiarity with multi-agent or distributed system design
• Experience building evaluation frameworks, test harnesses, or simulation-based training environments.
• Skilled in performance tuning (latency, throughput, cost efficiency).
• Proficiency in Python and ML/AI frameworks (PyTorch, TensorFlow, LangChain, Ray, etc.).
• Bonus: Experience with A/B testing, feedback loops, and integrating AI with front-end UX.