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Unilever

Full Stack Google AI Engineer

Unilever is hiring a Full Stack Google AI Engineer. Level rates it ; you can apply on Unilever.

Level's read on this job

  • On Level's scale, this AI engineer role is Works on AI ●●●○: the daily work is on AI products, without building the model.
  • Tools and skills the posting cites include Gemini, Vertex AI and RAG.
  • Level lists 258 other open roles at Unilever.
  • The role was posted in the last few days.

AI in this role

GeminiVertex AI
rag

Job Title: Full Stack Google AI Engineer

Work Level: 2A


JOB PURPOSE

We are looking for a Full Stack Google Artificial Intelligence Engineer to help build and scale enterprise artificial intelligence solutions on Google Cloud.

In this role, you will combine strong software engineering skills with modern artificial intelligence technologies to develop secure, scalable and production-ready applications. You will work across the application stack, from backend services and application programming interfaces to user-facing experiences and integrations with Google artificial intelligence services.

You will also help strengthen our in-house engineering capability by creating reusable components, patterns and standards that make it easier for teams to build and deploy artificial intelligence solutions consistently.

You will work closely with engineers, architects, platform specialists and product teams, giving you the opportunity to develop both your technical expertise and your experience delivering technology that solves real business problems.

Because here, we don’t just hire for jobs. We invite you to be part of something bigger.

KEY RESPONSIBILITIES

  • Design and develop end-to-end artificial intelligence-enabled applications on Google Cloud.
  • Build backend services, application programming interfaces and user-facing application components.
  • Integrate applications with Google artificial intelligence technologies, including Vertex AI and Gemini models.
  • Turn business and product requirements into practical, production-ready technical solutions.
  • Build reusable components, templates and engineering patterns that can be adopted across multiple products and use cases.
  • Develop applications with security, scalability, reliability, maintainability and observability in mind.
  • Apply modern software engineering practices, including automated testing, continuous integration and continuous delivery.
  • Work collaboratively with product teams, solution architects, artificial intelligence engineers and cloud platform teams.
  • Diagnose and resolve application, integration and performance issues across the solution.
  • Contribute to technical documentation, coding standards, code reviews and engineering best practices.
  • Share knowledge with other engineers and contribute to the continued growth of our Google Cloud and artificial intelligence engineering capability

REQUIRED QUALIFICATIONS

  • Hands-on software engineering experience using languages such as Java, JavaScript, TypeScript or similar modern programming languages.
  • Experience designing and building full-stack applications and web-based application programming interfaces.
  • Practical experience developing solutions on Google Cloud.
  • Experience developing applications using container-based or serverless architectures.
  • A good understanding of generative artificial intelligence and large language model application development.
  • Experience integrating artificial intelligence or machine learning capabilities into applications.
  • Experience with source control, automated testing and continuous integration and delivery practices.
  • A good understanding of cloud-native application architecture and modern software development practices.
  • Strong problem-solving skills and the ability to collaborate effectively with both technical teams and product stakeholders.

PREFERRED QUALIFICATIONS

  • Experience working with Vertex AI or Gemini models.
  • Experience with Google Cloud services such as Cloud Run, Cloud Functions, identity and access management, and application programming interface management.
  • Python development experience, particularly for artificial intelligence or cloud-based applications.
  • Experience building enterprise generative artificial intelligence applications using techniques such as retrieval-augmented generation, where artificial intelligence models use trusted enterprise information to improve their responses.
  • Exposure to artificial intelligence agents or multi-step artificial intelligence applications that can use tools, data and services to complete tasks.
  • Experience creating reusable software frameworks, shared engineering components or development accelerators.

At Unilever, inclusion is at the heart of everything we do. We welcome applicants from all walks of life and are committed to creating an environment where everyone can thrive/succeed. All applicants will receive fair and respectful consideration, and we actively support the growth and development of every employee.


Unilever is an Equal Opportunity Employer/Protected Veterans/Persons with Disabilities.

How we rate this

Full Stack Google AI Engineer at Unilever rates 64 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGGeminiVertex AI

Also common in AI engineer jobs at this level

Pay

Typical AI engineer job
$184k

This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. Walk me through how you've used Gemini in your day-to-day work.
  3. What are the limits of Vertex AI that you've run into, and how did you work around them?
  4. Describe a typical day in a role like this one: which parts run through AI directly?
  5. If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

  • List these exact terms on your resume: RAG, Gemini, and Vertex AI. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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