Data & AI Platform Engineer

Job not on LinkedIn

🔥 1 hour ago

🇮🇩 Indonesia – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🏗️ Platform Engineer

👻 Ghost score 10%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Wego.com

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2005

💼 Consulting

🏨 Hospitality

📦 Logistics

💰 $12M Series C - Wego on 2017-10

Consulting • Hospitality • Logistics

Wego. com is a leading travel marketplace and online travel agency focused on the MENA region that helps consumers plan and book flights, hotels, and discover travel inspiration. The company also operates a cashback and rewards platform and provides a business travel and expenses management solution, combining consumer-facing marketplace services with software tools for corporate travel.

📋 Description

• Build and operate ingestion systems and runtime infrastructure supporting analytics, data science and AI tools across Wego • Build reliable batch and streaming pipelines from source systems • Run pipelines and services on Kubernetes and cloud infrastructure • Build repeatable environments and safe release and rollback paths • Build systems enabling AI assistants and agents to use Wego data and APIs reliably • Work with data scientists on AI evaluations • Improve reliability and cloud cost against agreed service goals • Identify data loss, operational toil and slow delivery, validate impact with users and own fixes end to end • Build pipelines from APIs, databases and event sources • Handle schema changes, incremental loads and change data capture • Design retries, deduplication, backfills and replay • Monitor data freshness and completeness from source to consumer • Deploy and operate containerized pipelines and services • Manage resources, configuration, scaling and recovery with platform teams • Use infrastructure as code and automated delivery pipelines • Test changes and manage secrets and access • Define metrics, logs and alerts; diagnose incidents and fix recurring failures • Build tool and API integrations, agent loops, context and memory, retrieval, prompts and guardrails • Support model serving where needed • Build test sets, regression checks and monitoring of AI outputs with data scientists

🎯 Requirements

• 3–8 years of experience in data engineering, platform engineering or related infrastructure work • Experience owning ingestion systems in production, including failures, recovery and source schema changes • Hands-on experience operating Kubernetes workloads and cloud infrastructure • Experience with DevOps practices, automated deployments and infrastructure as code • Fluency in Python and SQL • Practical Linux, networking and container skills • Experience with APIs, databases and event systems, including batch or streaming ingestion • Understanding of delivery guarantees, idempotency and schema compatibility • Experience with Kubernetes and container tooling such as Docker and Helm • Experience with Google Cloud Platform or AWS • Experience with infrastructure tools such as Terraform • Practical knowledge of CI/CD, monitoring, alerting and incident diagnosis • Ability to build AI assistants and agents, including tool calling, agent loops, context and memory, retrieval and guardrails • Ability to evaluate AI assistants and agents with test sets and regression checks • Ability to identify worthwhile problems, validate their impact and own solutions through measurable impact • Ability to align priorities with stakeholders and work independently through ambiguity • Ability to test AI-generated code, investigate failure paths and own deployed output • Ability to provide tests and operational evidence before completing changes and fix defects after release • Ability to explain engineering tradeoffs practically and treat analysts, data scientists and product teams as users • Experience building AI assistants, agents, evaluations or model services is a plus • Travel-domain experience is a plus

🏖️ Benefits

• Fully remote work arrangement • Opportunities to work on high-impact data, platform and AI tooling projects • Collaboration with analysts, data scientists and engineering teams • Professional experience with cloud infrastructure, Kubernetes, DevOps and AI systems • Travel-domain experience is a plus

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume