Editorial summary

The company is seeking an Applied AI Engineer responsible for transforming model capabilities into functional product features, overseeing end-to-end development from model behavior design to system deployment and performance optimization. The role involves building AI features, designing prompts and workflows, and converting raw model outputs into structured, user-friendly outputs. Candidates should have expertise in machine learning, systems integration, and product development. The position offers a salary of 8,500.

This summary is AI-generated and may contain inaccuracies. Please refer to the full job description below.

Job description

[This job id 19812 first appeared in Job-Q.com on 14 Jul 2026]

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.

Job Duties:

• 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
• ML models in production meet expected accuracy, latency, and reliability targets.
• Production issues are identified quickly, debugged effectively, and root causes 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.

Technical experience/Requirements:
Python, PyTorch / JAXLLMs (OpenAI-style APIs, LLaMA, Qwen, etc.), Inference / serving (e.g. vLLM), Vector DB

• 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

Interested Candidates may apply directly to Patrina@eps.com.sg (Reg ID: R1103695) for immediate processing

EPS Consultants Pte Ltd | 95C5630

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