Applied AI Engineer

EPS CONSULTANTS PTE LTD; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

EPS CONSULTANTS PTE LTD in Singapore is seeking an Applied AI Engineer to turn model capabilities into real product behavior. You will own problems end-to-end—from shaping model behavior to building the surrounding systems—in production.

You will work at the intersection of machine learning, systems, and product to make AI work for users, not just in demos, but in real-world usage, collaborating with product and engineering teams to ship reliable ML-powered features.

Qualifications

• 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

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
• 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

Skills

ML fundamentals

Production code

Ambiguous problem solving

Shipping mindset

Collaboration

Tools

Vector DB

vLLM

OpenAI-style APIs

LLaMA

Job description

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