Product Owner

Agoda operates a digital travel platform that connects people to destinations and experiences through hotels, holiday properties, flights, and activities worldwide.

• Define and evolve the product vision and roadmap for Agoda’s ML & LLM Ops platforms;
• Shape how teams productionize ML models and LLM-powered applications;
• Identify platform gaps, scalability constraints, and optimization opportunities;
• Drive the evolution of our platform to support AI-native development workflows and coding agents;
• Gather requirements from ML engineers, data scientists, backend engineers, and AI application teams;
• Translate complex technical needs into clear product requirements and prioritization frameworks;
• Proactively identify opportunities to reduce friction, improve reliability, and increase deployment velocity;
• Balance experimentation flexibility with governance, safety, and cost efficiency;
• Own features end-to-end: concept, specification, prioritization, implementation, and post-launch analysis;
• Partner closely with engineering to balance platform scalability, performance, and technical debt;
• Ensure platform capabilities support monitoring, evaluation, retraining, versioning, and observability of ML/LLM systems;
• Drive adoption through documentation, training, and internal evangelism;
• Collaborate with engineering to level up Agoda’s platforms to be more compatible with AI coding assistants and autonomous agents;
• Ensure platform APIs, tooling, and abstractions enable meaningful AI-generated code contributions;
• Help define guardrails and workflows that allow safe, reliable AI-assisted development.

• 5+ Years of experience in ML engineering, data science, platform engineering, or a related technical domain;
• 2+ Years of technical product or program management experience in fast-paced, high-scale environments;
• Experience working with ML Ops and/or LLM Ops concepts (model lifecycle, evaluation, monitoring, versioning, deployment pipelines);
• Strong understanding of ML systems and model lifecycle management;
• Familiarity with LLM application patterns (RAG, fine-tuning, prompt management, evaluation frameworks, etc.);
• Comfortable discussing architecture, APIs, infrastructure trade-offs, and scalability concerns with senior engineers;
• Strong analytical mindset with the ability to reason about system design, performance, and cost trade-offs;
• Strong cross-functional leadership skills and ability to influence without authority;
• Comfortable operating in ambiguity and rapidly evolving technological landscapes;
• Excellent communication skills, with the ability to translate between business, platform engineering, and AI application teams;
• Proactive, structured thinker with a strong problem-solving mindset;
• High ownership and bias for impact.

• Relocation support is available.