Applied AI Engineer

Our Client is an AI start-up company. As an Applied AI Engineer, you'll bridge cutting-edge AI models with real-world products. This role sits at the intersection of machine learning, software engineering, and product development, with a focus on transforming AI capabilities into intuitive, dependable experiences that deliver real value to users.

Responsibilities

• Design, build, and ship AI-powered features from end to end—from model integration to production deployment and user experience.
• Develop and refine prompts, tool usage, memory systems, and agent workflows to improve AI performance.
• Transform raw model outputs into structured, reliable, and predictable product behavior.
• Investigate and resolve issues across the AI stack, including models, orchestration, infrastructure, and user experience.
• Improve system performance by optimizing latency, reliability, scalability, and operational cost.
• Build lightweight evaluation frameworks to measure model quality and production performance.
• Partner closely with product managers and engineers to translate ambiguous product requirements into robust AI solutions.

Requirements

• Strong foundation in machine learning and modern deep learning architectures.
• Hands-on experience training, fine-tuning, evaluating, or deploying machine learning models.
• Ability to write clean, maintainable, production-quality Python code.
• Comfortable working across the full AI stack—from models and infrastructure to product integration.
• Strong analytical and problem-solving skills, with the ability to navigate ambiguity.
• A builder's mindset with a focus on shipping quickly, iterating based on feedback, and continuously improving systems.
• Technologies and tools: Python, PyTorch / JAX, Large Language Models (OpenAI APIs, Llama, Qwen, and similar models), Inference and serving frameworks (e.g., vLLM) and Vector databases

Success Looks Like

• AI-powered features consistently meet production targets for quality, latency, and reliability.
• Production issues are identified, diagnosed, and resolved efficiently with lasting improvements.
• Training pipelines, inference systems, and supporting infrastructure are scalable, reproducible, and maintainable.
• Strong collaboration with engineering, product, and research teams to deliver high-impact AI capabilities.
• Model and system improvements are driven by measurable user outcomes and real-world performance data.