AI Engineer

Key ResponsibilitiesBuild and ship AI features end-to-end (model → system → user experience)Design and iterate on prompts, tools, memory, and agent workflowsTurn raw model outputs into structured, reliable, and predictable behaviorsDebug issues across the full stack (model, orchestration, infra, UX)Optimize for latency, cost, and production reliabilityDevelop lightweight evaluation frameworks to measure real-world performanceWork closely with product and engineering to translate ambiguous problems into working systemsTech StackPythonPyTorch / JAXLLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)Inference/serving (e.g. vLLM)Vector DBIdeal ExperienceStrong foundation in machine learning and modern neural network architectures.Hands-on experience with training, fine-tuning, or deploying ML modelsAbility to write clean, production-quality codeComfort working across abstraction layers (model → infra → product)Strong problem-solving skills in ambiguous, fast-moving environmentsBias toward shipping, iteration, and continuous improvementOutcomesML 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.