Applied AI Engineer

As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production.

This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage.

Responsibilities:

• Build and deliver AI features end-to-end, spanning model development, system integration, and the final user experience.
• Design and continuously refine prompts, tools, memory systems, and agent workflows to improve model capabilities and task performance.
• Transform raw model outputs into structured, reliable, and predictable behaviors that meet product requirements.
• Diagnose and resolve issues across the full AI stack, including models, orchestration, infrastructure, and user experience.
• Optimize AI systems for latency, cost efficiency, scalability, and production reliability.
• Build lightweight evaluation frameworks and metrics to measure model and system performance in real-world scenarios.
• Partner closely with product and engineering teams to turn ambiguous user and business problems into practical, production-ready AI solutions.

Tech Stack

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

Experience

• Strong understanding of machine learning fundamentals and modern neural network architectures.
• Hands-on experience training, fine-tuning, deploying, or optimizing machine learning models.
• Ability to write clean, maintainable, and production-grade software.
• Comfort working across multiple layers of the AI stack, from models and infrastructure to product experiences.
• Strong analytical and problem-solving skills, particularly when working through ambiguous and rapidly evolving challenges.
• Bias toward execution and shipping, with a mindset of rapid experimentation, iteration, and continuous improvement.

Outcomes

• Production ML models consistently meet defined accuracy, latency, and reliability targets.
• Production issues are detected, diagnosed, and resolved quickly, with root causes addressed to prevent recurrence.
• Data pipelines, training workflows, and inference systems are robust, reproducible, scalable, and maintainable.
• Collaborates effectively across engineering, product, and research teams to deliver reliable and impactful ML-powered features.
• Model and system improvements are guided by real-world usage, feedback, and measurable performance gains.
  

We regret that only shortlisted candidates will be notified.

GMP Technologies (S) Pte Ltd | EA License: 11C3793 | EA Personnel: Bautista Gia Grace De Guzman | Registration No: R23111973

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