$70/day
About Extenteam Extenteam is an AI-driven operations platform for short-term rental property ... We are hiring a Product Engineer dedicated to improving the platform layer that our Shared Services ...
$70/day
About Extenteam Extenteam is an AI-driven operations platform for short-term rental property ... We are hiring a Product Engineer dedicated to improving the platform layer that our Shared Services ...
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Ai Platform Engineer information
What is an AI Platform Engineer?
AI Platform Engineers are technology professionals who design, build, and maintain the infrastructure that supports the development, deployment, and scaling of artificial intelligence (AI) and machine learning (ML) models. They work closely with data scientists and software engineers to ensure that AI solutions can run efficiently and securely in production environments. Their responsibilities often include managing cloud or on-premises platforms, automating workflows, and implementing best practices for model versioning, monitoring, and resource optimization.
How does an AI Platform Engineer typically collaborate with data scientists and software engineers in a project environment?
AI Platform Engineers often serve as a bridge between data scientists and software engineers, ensuring that machine learning models are seamlessly integrated into scalable, production-ready systems. They work closely with data scientists to understand model requirements and deployment needs, and with software engineers to embed these models within applications and services. This collaboration involves frequent communication, joint troubleshooting, and participation in code reviews to maintain a robust and efficient AI infrastructure.
What are the key skills and qualifications needed to thrive as an AI Platform Engineer, and why are they important?
To thrive as an AI Platform Engineer, you need strong programming skills (especially in Python and Java), a background in computer science or related fields, and experience with machine learning frameworks. Familiarity with cloud platforms (like AWS, Azure, or GCP), containerization tools (Docker, Kubernetes), and CI/CD systems is typically required, along with certifications such as Google Cloud Professional Machine Learning Engineer. Excellent problem-solving, collaboration, and communication skills help you integrate AI solutions across teams and projects. These competencies ensure the efficient development, deployment, and maintenance of scalable AI systems in dynamic production environments.
What is the difference between Ai Platform Engineer vs Data Engineer?
| Aspect | Ai Platform Engineer | Data Engineer |
|---|---|---|
| Credentials | Bachelor's in CS, AI, or related; experience with cloud platforms | Bachelor's in CS, Data Science, or related; experience with databases and ETL tools |
| Work Environment | Developing AI infrastructure, deploying ML models, working with cloud services | Building data pipelines, managing data storage, ensuring data quality |
| Industry Usage | Tech companies, AI startups, cloud providers | Finance, healthcare, e-commerce, any data-driven industry |
While both roles involve working with data and cloud platforms, Ai Platform Engineers focus on building and maintaining AI infrastructure and deploying machine learning models. Data Engineers primarily develop data pipelines and manage data storage. The roles often collaborate but serve different core functions within AI and data ecosystems.
How to become an AI platform engineer?
To become an AI platform engineer, you should have a strong background in computer science, software engineering, or related fields, with expertise in machine learning frameworks, cloud computing, and programming languages like Python or Java. Gaining experience with AI tools, data management, and infrastructure deployment is essential, often supported by certifications in cloud platforms such as AWS or Azure. Building a portfolio of projects and staying updated on AI and DevOps practices can also enhance your qualifications.
What does an AI platform engineer do?
An AI platform engineer designs, develops, and maintains the infrastructure and tools needed to deploy and manage artificial intelligence models at scale. They work with cloud services, programming languages, and machine learning frameworks to ensure efficient model training, deployment, and monitoring in production environments.
What is the salary of AI platform engineer?
The salary of an AI platform engineer typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in cloud platforms and machine learning may earn higher compensation.
What are popular job titles related to Ai Platform Engineer jobs in Micronesia?
For Ai Platform Engineer jobs in Micronesia, the most frequently searched job titles are:
What job categories do people searching Ai Platform Engineer jobs in Micronesia look for?
The top searched job categories for Ai Platform Engineer jobs in Micronesia are:
$70/day
Full-time
Re-posted 6 days ago
Job description
About Extenteam
Extenteam is an AI-driven operations platform for short-term rental property managers. We handle guest communication and daily workflows for over 80,000 properties through a proprietary platform that combines AI automation with human-in-the-loop oversight. We are headquartered in Miami.
We are hiring a Product Engineer dedicated to improving the platform layer that our Shared Services team uses every day.
The RoleYou will report to the Head of Engineering. Your work focuses on Shared Services platform improvements. Priorities are set jointly with the Head of Operations, who owns the operational signal driving what needs to be built.
You will do two things:
- Close incoming platform tickets from Shared Services. Agents, Team Leads, and operations report platform issues daily. Some are quick fixes. Some are workflow bugs. Some are configuration issues. You triage, diagnose, and ship the fixes.
- Identify patterns and ship platform improvements. When multiple agents report the same friction, that is not a ticket - that is a product problem. You investigate the underlying cause and ship a durable platform improvement that prevents the same issue from happening again. When a client complaint traces back to repeated agent failures, you ask "why is the platform allowing this?" and fix it at the source.
Every ship goes through the standard product and engineering process product filters for product coherence, design filters for UX consistency, standard code review applies. You work inside the same guardrails as any other engineer
What You Will BuildExamples of platform work this role would own:
- Pre-send rule layer that catches compliance violations before AI or agent replies reach the guest
- Agent tooling that surfaces the right SOP at the right moment based on ticket type and client
- A repeat-failure detection layer that flags when the same issue is being escalated across multiple agents or shifts
- Workflow improvements that reduce cognitive load on agents during high-volume periods
- Integrations between platform and the tools agents rely on (Aircall, HubSpot, PMS platforms)
- Instrumentation that connects client complaints back to specific platform gaps
- Not a ticket queue firefighter. That is other roles on the operations side.
- Not a shadow engineering team. You report to and work within Engineering.
- Not an independent operator building outside our platform. Everything you ship goes through product
- 5+ years of software engineering experience, with at least 2 years shipping production code
- Direct experience building internal tools, agent-facing platforms, or operational systems
- Product mindset - comfortable scoping problems, asking users the right questions, and prioritizing tradeoffs
- Track record of shipping fast while working within a product and design process
- Experience working asynchronously with distributed teams
- Strong fluency in Python and/or TypeScript
- Comfortable with agentic coding tools (Claude Code, Cursor, Replit Agent)
- Database fluency (PostgreSQL preferred), able to write your own queries and design schemas
- API integration experience (REST, webhooks, third-party SaaS APIs)
- Prompt engineering experience with production AI systems (OpenAI, Anthropic, or similar)
- Experience building tools for contact center, customer support, or operations teams
- Short-term rental, hospitality, or property management domain knowledge
- Track record of deploying AI agents in production
- Reports to the Head of Engineering
- Priorities set jointly with the Head of Operations based on operational signal
- Product decisions filter through the Head of Product
- Design decisions go through our design team
- Weekly syncs with Engineering, Operations, and Product
- We expect 5 hours of overlap with Miami time (Eastern Time) daily
Base range is $70-105K USD depending on location and experience.
LocationRemote, based in Europe or South America.