Product Engineering Team Lead

Nicoll Curtin; Hong Kong; Remote; HKD 727,000 - 1,039,000; Full time

Benefits offered by this job

Fully remote work

Job summary

Nicoll Curtin partners with a tech-led business building an AI product for real-world tasks and decision-making. This remote, permanent role focuses on designing and operating production ML systems built around end-to-end pipelines from training to inference.

You’ll work on training, inference, and evaluation pipelines, LLM-based workflows, and performance optimisations across latency, cost, and reliability, with monitoring and production stability as core priorities.

Qualifications

• Production ML experience with real users and ownership.
• Shipped ML systems end-to-end in production.
• Experience with modern LLMs beyond API integration.
• Ability to debug model behaviour and optimise latency/cost.
• Familiarity with monitoring, observability and evaluation frameworks.

Responsibilities

• Design, train, infer, and evaluate ML pipelines for production.
• Develop and operate LLM-based systems and agent-style workflows.
• Optimise performance across latency, cost, and reliability.
• Maintain production monitoring, logging and system stability.

Skills

ML production

Python

LLM workflows

Performance optimisation

Tools

PyTorch

Docker

Kubernetes

Job description

Remote (United Kingdom) | Permanent | Flexible compensation + equity

We’re working with a well-established, tech-led business that is building a new AI product focused on real-world tasks, workflows, and decision-making.

This is a small, high-calibre team building systems where model capability is transformed into reliable, production-grade ML systems, with a strong emphasis on ownership, iteration, and real-world performance.
The product focuses on:

Long-running AI workflows

Persistent context across interactions

Multi-step reasoning and task execution

Integration with external tools and systems

The core challenge is designing ML systems that can behave reliably in production, even when model outputs are inherently non-deterministic.

The Role

This role sits at the core of the ML layer powering the product.The focus is on designing and operating systems that enable:

ML models to run reliably in production

End-to-end pipelines from training to inference

Continuous evaluation and iterative improvement

Systems that perform consistently under real usage conditions

You’ll be working on:

Training, inference, and evaluation pipelines

LLM-based systems and agent-style workflows

Optimising performance across latency, cost, and reliability

Production monitoring, logging, and system stability

What They Care About

The hiring bar is centred around real production ML experience:Whether you have:

Shipped ML systems used by real users

Owned ML systems end-to-end in production

Worked with modern LLMs beyond simple API integration

Your exposure to practical challenges such as:

Model behaviour debugging and failure analysis

Latency, throughput, and cost optimisation

Monitoring, observability, and evaluation frameworks

Scaling ML systems in production environments

Tech Environment

Python (core ML and backend language)

PyTorch / modern ML frameworks

GPU-based training and inference

Docker and Kubernetes

The emphasis is on how you build and operate ML systems, rather than specific tools.

Team & Working Style

Fully remote-first, work from anywhere

Small, highly capable engineering team

Strong emphasis on ownership and delivery

Fast iteration cycles with real user feedback

Comfortable working in evolving systems and making pragmatic decisions