AI Platform Engineer

Technologies we use

Optional

• AWS
• Microsoft Azure
• Google Cloud Platform
• GitHub Copilot
• Cursor
• Claude
• Codeium
• Go
• Java
• Python
• TypeScript

About the project

For our client we are looking for AI Platform Engineer.

Client is the emerging leader in the $100B+ cloud communications platform (CPaaS) market. Customers like Airbnb, Viber, Whatsapp, Snapchat, and many others depend on our APIs and SDKs to connect with their customers all over the world. As businesses continue to shift to a real-time, customer-centric communications model, we are experiencing a time of impressive growth.

Why this role matter.

The Engineering team at Client is a fast-growing group of talented engineers. They face multiple challenges associated with high performance, distributed systems and processing large volumes of data in real time.

The client's tooling team builds the tools, collects metrics and generates insights that help our Engineering department work on a better platform, improving engineering productivity, delivery efficiency and developer experience.

Some of these tools are built around client's IDP/service catalog and work along the whole SDLC. They are also responsible for AI adoption across the company, evaluating and rolling out AI tooling and practices that make our engineers more productive.

Client's team works on an exciting mix of both greenfield and existing projects, giving engineers the opportunity to explore new technologies and have a direct, visible impact on how the whole Engineering department works.

This is how we organize our work

This is how we work

• in house
• you have influence on the choice of tools and technologies
• you have influence on the technological solutions applied
• you have influence on the product

Your responsibilities

• Design, implement, and integrate AI-powered solutions that improve developer productivity and engineering efficiency.
• Evaluate and adopt modern AI tooling supporting software development and SDLC processes.
• Collaborate with software engineering, platform engineering, DevOps, and architecture teams.
• Integrate AI capabilities into internal engineering platforms, developer portals, and delivery pipelines.
• Improve CI/CD, testing, automation, and release processes using AI-driven approaches.
• Support and optimize developer workflows and engineering tools.
• Identify opportunities to accelerate software delivery through automation and AI.
• Contribute to the evolution of developer experience and engineering enablement initiatives.
• Work with cloud-native platforms and modern engineering ecosystems.

Our requirements

• Experience working with AI tools, platforms, or solutions that enhance developer productivity and engineering effectiveness.
• Practical experience building, integrating, customizing, or implementing AI-powered tools and applications.
• Strong understanding of Software Development Life Cycle (SDLC) processes.
• Experience with software delivery automation, CI/CD pipelines, testing, and release management.
• Experience integrating platforms, tools, or services within engineering ecosystems.
• Excellent communication and collaboration skills.
• Fluent English (C1 or higher).

Optional

• Experience with Kubernetes and cloud platforms (AWS, Azure, or GCP).
• Experience in Platform Engineering, DevOps, Developer Experience (DevEx), or Software Engineering.
• Knowledge of Internal Developer Platforms (IDP).
• Familiarity with AI coding assistants such as GitHub Copilot, Cursor, Claude, Codeium, or similar solutions.
• Experience with Infrastructure as Code and automation frameworks.
• Knowledge of Go, Java, Python, or TypeScript.
• Experience measuring and improving engineering productivity metrics.

What we offer

• Contract: B2B directly with US company.
• Rate: up to 170 PLN / h.
• 100% remote.
• Polish time zone.
• Polish public holidays.
• Recruitment process: 1-2 technical calls.

Recruitment stages

• 1-2 technical calls

Note for Candidates

Before submitting your CV, please ensure it clearly highlights:

• Experience with AI tools that improve developer productivity and engineering effectiveness.

• AI implementation, integration, or customization projects.

• SDLC, automation, CI/CD, testing, and release process experience.

• Platform Engineering, DevOps, Kubernetes, and cloud experience.

• Concrete examples of AI initiatives and measurable business or engineering outcomes.