AI Engineer

As an AI Engineer, your mission is to bridge the gap between cutting-edge model capabilities and tangible product value. You will own the AI lifecycle end-to-end—shaping model behavior, engineering the surrounding systems, and ensuring flawless performance at scale. This cross-functional role sits at the intersection of machine learning, system architecture, and product design, with a singular focus: making AI robust, reliable, and deeply impactful for real-world users, not just in demos.

Core Responsibilities

• End-to-End Feature Delivery: Own the entire pipeline from raw model to user experience, building and shipping production-ready AI features.
• Agentic Design & Orchestration: Architect, iterate, and optimize prompts, tool-use, memory retrieval, and autonomous agent workflows.
• Behavior Standardization: Transform unpredictable, raw model outputs into structured, deterministic, and dependable system behaviors.
• Full-Stack Troubleshooting: Debug and resolve complex issues across the entire stack—spanning models, orchestration layers, infrastructure, and the UX.
• Performance Engineering: Fine-tune system architecture for minimal latency, optimal cost-efficiency, and high production uptime.
• Data-Driven Evaluation: Build and implement lightweight, effective evaluation frameworks to continuously measure and improve real-world model accuracy.
• Collaborative Problem Solving: Partner with product and engineering teams to turn ambiguous, high-level challenges into functional code and systems.

Technical Toolkit

• Languages: Python
• Frameworks: PyTorch, JAX
• Models: Commercial and open-source LLMs (OpenAI APIs, LLaMA, Qwen, etc.)
• Infrastructure: Inference and serving frameworks (e.g., vLLM)
• Data: Vector databases and retrieval systems

Who You Are

• ML Foundations: You possess a deep understanding of machine learning principles and modern neural network architectures.
• Practical Experience: You have a proven track record of training, fine-tuning, or deploying large models into production environments.
• Software Craftsmanship: You write clean, maintainable, and production-grade code.
• Full-Stack Adaptability: You easily pivot across different abstraction layers, moving comfortably from low-level model infra to high-level product logic.
• Thrives in Ambiguity: You are a natural problem solver who excels in fast-paced, rapidly evolving startup environments.
• Execution Mindset: You maintain a strong bias toward action, rapid iteration, and continuous shipping.