AI Engineer

Key Responsibilities

• Build and ship AI features end-to-end (model → system → user experience)
• Design and iterate on prompts, tools, memory, and agent workflows
• Turn raw model outputs into structured, reliable, and predictable behaviors
• Debug issues across the full stack (model, orchestration, infra, UX)
• Optimize for latency, cost, and production reliability
• Develop lightweight evaluation frameworks to measure real-world performance
• Work closely with product and engineering to translate ambiguous problems into working systems

Tech Stack

• Python
• PyTorch / JAX
• LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)
• Inference/serving (e.g. vLLM)
• Vector DB

Ideal Experience

• Strong foundation in machine learning and modern neural network architectures.
• Hands-on experience with training, fine-tuning, or deploying ML models
• Ability to write clean, production-quality code
• Comfort working across abstraction layers (model → infra → product)
• Strong problem-solving skills in ambiguous, fast-moving environments
• Bias toward shipping, iteration, and continuous improvement

Outcomes

• ML models in production meet expected accuracy, latency, and reliability targets.
• Production issues are identified quickly, debugged effectively, and root causes are addressed.
• Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
• Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
• Iterations on models and systems are driven by real-world signals and measurable improvements.