Operations Delivery Lead

Remoat Teams; Singapore; On-site; SGD 89,000 - 179,000; Full time

Job summary

Remoat Teams is seeking an AI-native delivery operator to own and improve client operations in recruitment or AI data labelling. You will define delivery plans, oversee workforce readiness, maintain quality controls and manage client communication in a fast-moving startup environment.

This role requires hands-on ownership, willingness to adapt to changing priorities, and the ability to learn new domains quickly.

Qualifications

• Delivery experience in recruitment operations or AI data labelling.
• Launch or improve delivery against real deadlines.
• Explain end-to-end workflow, workforce requirements and quality controls.
• Be able to learn adjacent workflows and domains quickly.
• Show use of LLMs or agentic tools to build or improve real workflows.

Responsibilities

• Translate client brief into an executable plan with success criteria.
• Build sourcing, qualification and activation approaches for the assignment.
• Design assessments or quality checks with domain experts.
• Track throughput, rework and capacity; propose improvements.
• Plan teams, tools and dependencies to meet agreed dates.
• Create clear instructions, checklists and handoffs.
• Maintain a clear operating cadence with stakeholders.
• Utilize AI-native tools to analyze performance and optimize workflows.

Skills

Delivery ownership

Launch & improvement

Domain understanding

Metrics-driven

AI-native competence

Client judgment

Hands-on ownership

Tools

Claude Code

Codex

GrokBot

Dots in ChatGPT

Job description

Own delivery from client brief to reliable output

Remoat Teams is looking for an AI-native delivery operator to run and improve client operations in recruitment or AI data labelling. The assignment will depend on your experience and the business need. You should bring direct delivery ownership in at least one relevant domain and the ability to learn the specific requirements of the other.

You will own the delivery plan, workforce readiness, workflow performance, quality controls and client communication. For recruitment, the outcome is a reliable hiring process and qualified starts. For AI data labelling, it is accepted work that meets the brief, with controlled rework and dependable throughput. Work closely with product and engineering to improve how the service runs.

We want someone who has built and changed processes, not only maintained an established account. You should be comfortable with an incomplete playbook, use AI-native tools throughout your workflow and take responsibility for getting the operation ready.

Read this first. the intensity

This is an intense startup role. We need someone who is comfortable with changing priorities, short turnaround times and taking responsibility for a problem through to resolution. You will be close to the detail and expected to make decisions, communicate clearly and move work forward without waiting for a complete playbook.

Weekend work is normal here, not an occasional exception. If you want a predictable Monday-to-Friday role, tightly defined tasks or a job where someone else makes the difficult operating decisions, do not apply. We are looking for people who want the responsibility of building a startup operation and are willing to match that intensity. Expect direct feedback, changing priorities and a high bar for usable work. An impressive presentation does not compensate for a process that fails in practice. We will discuss the working arrangement, required time-zone overlap and availability expectations clearly during the hiring process. The exact schedule and hours will be confirmed before an offer; we do not want candidates discovering the commitment after joining.

What the work looks like

• Client requirements. Confirm the volume, deadline, quality standard and acceptance criteria. Translate the brief into an executable plan, using the appropriate definition of success for recruitment or data labelling.
• Workforce readiness. Build the sourcing, qualification and activation approach needed for the assignment. Track qualified candidates through to hiring starts or assess contributor capacity and suitability for labelling tasks.
• Quality and decision rules. Design role-relevant assessments or task-level quality checks with domain experts. Define what is accepted, what needs review and how uncertain or disputed decisions are resolved.
• Workflow performance. Track conversion and waiting time in recruitment or throughput, acceptance and rework in data labelling. Diagnose the constraint and give the team specific improvements to implement.
• Capacity and launch readiness. Plan the people, tools and dependencies needed to meet an agreed date. Model assumptions explicitly and accelerate capacity or quality risks before they threaten delivery.
• Reusable playbooks. Create clear instructions, checklists and handoffs. Adapt them to the client, language, task and quality requirements rather than assuming every delivery operation is the same.
• Client communication. Maintain a clear operating cadence, report risks early and close gaps across delivery, recruiting, product and engineering.
• AI-native delivery. Use LLMs and agentic tools to analyse performance, build workflows and reduce repetitive work. Understand how to use Claude Code, Codex, GrokBot and Dots within ChatGPT and how to select tools for the task. Validate outputs, protect client data and retain human review where quality or exceptions require judgment.

Scope of the role

You own client delivery and operating execution in the assigned domain. Product direction, commercial pricing and contract terms stay with the relevant business owners. Define operational needs, build suitable lightweight workflows using approved AI tools and help validate solutions. This does not make you the owner of the whole product roadmap or require you to write all production software.

The operator we want

• Relevant delivery experience. Bring substantial hands-on ownership in recruitment operations, AI data labelling or a comparable technology-enabled service. Show the scale, complexity and outcomes of the work you personally ran.
• Launch and improvement experience. Show how you launched or improved delivery against a real deadline, ideally across multiple clients or workstreams.
• Domain understanding. In your strongest domain, explain the end-to-end workflow, workforce requirements and quality controls. Be candid about the domain you have not yet operated and how you would learn it.
• Measured improvement. Explain a change in time to fill, fill rate, accepted-output throughput, quality, rework or delivery cost. State the baseline and your own contribution rather than combining unlike measures.
• AI-native competence. Demonstrate more than chatbot-assisted writing. Show how you used LLMs or agentic tools to build or improve a real workflow, how you checked it and what changed. Be ready to discuss Claude Code, Codex, GrokBot and Dots within ChatGPT, including tool selection and limitations.
• Clear client judgment. Communicate risks specifically and challenge an unrealistic assumption while proposing a workable alternative.
• Hands-on ownership. Execute, make sound decisions and adapt quickly to evidence and direct feedback.

Relevant backgrounds include Service Delivery Lead, AI Data Operations Lead, Data Annotation Operations Manager, Recruitment Delivery Manager or RPO Operations Lead. Depth in one relevant domain matters more than superficial familiarity with both. We will assess your ability to run the assigned operation and learn adjacent workflows.

What strong performance looks like

In the first month, take ownership of an agreed delivery priority, establish a credible capacity and quality view, identify the most important gaps and put practical AI-native improvements into use. Specific commitments will be agreed against the actual client brief, baseline and available resources.

Our proposed selection process includes an introductory conversation, a short practical exercise, a team discussion and a final conversation. We will explain the exercise scope and time expectation in advance. AI tools may be used where disclosed; we will ask you to explain and adapt your work.