Gemini Agent Platform AI Engineer
Unilever is hiring a Gemini Agent Platform 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.
- The posting mentions Gemini, Vertex AI and RAG among its tools and skills.
- Unilever has 231 other open roles on Level.
- Posted in the last few days.
AI in this role
Job Title: Gemini Agent Platform AI Engineer
Job Location: Bangalore, India
Function: Digital & Technology – HAP
Reports: Senior Manager, HAP
Scope: Global
Terms & Conditions: Full-time, permanent. Applicants must have the right to work in India.
About Unilever:
Be part of the world’s most successful, purpose-led business. Work with brands that are well-loved around the world, that improve the lives of our consumers and the communities around us. We promote innovation, big and small, to make our business win and grow; and we believe in business as a force for good. Unleash your curiosity, challenge ideas and disrupt processes; use your energy to make this happen. Our brilliant business leaders and colleagues provide mentorship and inspiration, so you can be at your best. Every day, nine out of ten Indian households use our products to feel good, look good and get more out of life – giving us a unique opportunity to build a brighter future.
Every individual here can bring their purpose to life through their work. Join us and you’ll be surrounded by inspiring leaders and supportive peers. Among them, you’ll channel your purpose, bring fresh ideas to the table, and simply be you. As you work to make a real impact on the business and the world, we’ll work to help you become a better you.
JOB PURPOSE
The Gemini Agent Platform AI Engineer will help accelerate the adoption of Google Cloud, Gemini and agent-based artificial intelligence technologies across Unilever.
This is a hands-on engineering role with a strong product engagement dimension. You will partner with product and engineering teams to understand their existing applications and technology landscape, identify opportunities to migrate or modernize solutions onto Google Cloud, and help define and engineer the target solutions.
Alongside application migration and modernization, you will design and build artificial intelligence and intelligent agent solutions using Google's technology ecosystem. A key part of the role will be developing reusable engineering components, patterns and implementation approaches that can support multiple enterprise use cases.
You will work across the complete development lifecycle, from discovery and technical design through development, testing, deployment, monitoring and continuous improvement, helping teams turn ideas and prototypes into secure, scalable and production-ready solutions.
You’ll join a collaborative environment where engineers, architects and product teams work together to solve meaningful problems, experiment with emerging technologies and shape how Google and artificial intelligence capabilities are adopted at enterprise scale.
Because here, we don’t just hire for jobs. We invite you to be part of something bigger.
KEY RESPONSIBILITIES
- Partner with product and engineering teams to understand existing applications, technical requirements and modernisation needs, and identify appropriate opportunities to adopt Google Cloud technologies.
- Support the migration and modernisation of applications onto Google Cloud, working with product teams and architects to define target architectures and practical engineering approaches.
- Design, develop and deploy artificial intelligence applications and intelligent agent solutions using Google Gemini and Google Cloud technologies.
- Take solutions through the complete engineering lifecycle, from experimentation and technical design through development, testing, deployment, monitoring and continuous improvement.
- Build reusable components, frameworks and engineering patterns that accelerate future artificial intelligence and Google Cloud implementations.
- Develop integrations between intelligent agents, enterprise applications, application programming interfaces and data sources.
- Design solutions that securely connect artificial intelligence models with trusted enterprise information and services.
- Build and orchestrate workflows involving artificial intelligence models, software tools, business applications and cloud services.
- Evaluate technical approaches based on engineering feasibility, scalability, reliability, security, operational readiness and cost.
- Apply strong software engineering practices across source control, automated testing, deployment automation, version management and release processes.
- Implement appropriate security, identity and access management, monitoring, observability and responsible artificial intelligence practices.
- Evaluate and improve the quality, reliability and technical performance of artificial intelligence and agent-based solutions.
- Work closely with architects, product teams and engineering partners to convert business requirements and early prototypes into production-ready solutions.
- Create clear technical documentation, reusable implementation guidance and engineering standards that enable other teams to adopt proven solutions and patterns.
- Support rapid experimentation and proof-of-concept development while ensuring successful solutions have a clear path towards production and scale
REQUIRED QUALIFICATIONS
- Strong software engineering experience with the ability to design, develop and productionise cloud-based applications.
- Hands-on experience designing, developing and deploying solutions on Google Cloud.
- Strong understanding of the software development lifecycle, covering solution design, development, testing, deployment, monitoring and production operations.
- Experience developing applications using generative artificial intelligence or large language models.
- Understanding of intelligent agent concepts, including tool use, orchestration, grounding and context management.
- Experience with cloud-native application architectures, application programming interfaces and integration patterns.
- Experience with modern software engineering practices, including source control, automated testing, continuous integration and automated deployment.
- Ability to understand existing application architectures and define appropriate approaches for migration and modernisation onto Google Cloud.
- Ability to engage effectively with product, architecture and engineering teams and translate business and technical requirements into practical engineering solutions.
- Ability to take prototypes and early-stage solutions through engineering into secure, scalable, reliable and production-ready solutions.
PREFERRED QUALIFICATIONS
- Hands-on development experience with Google Gemini and Vertex AI.
- Experience developing cloud or artificial intelligence solutions using Python.
- Experience with Google technologies and frameworks for building intelligent agents.
- Experience with retrieval-augmented generation, embeddings, semantic retrieval and vector search.
- Experience with artificial intelligence evaluation, monitoring, observability and responsible artificial intelligence controls.
- Experience creating reusable artificial intelligence components, engineering frameworks or platform capabilities.
- Experience with application migration and modernisation on Google Cloud.
- Experience with cloud-native technologies, including containers and serverless architectures.
- Experience with enterprise application integration and application programming interfaces.
- Experience working across global, cross-functional product and engineering teams.
- Experience working with external technology or delivery partners.
ADDITIONAL INFORMATION
- This role operates with a global scope and will involve close collaboration with product teams, architects, platform teams and engineering partners.
- Success in this role means being equally comfortable in an engineering discussion and in a product conversation. You will need to understand how an application works today, identify how Google technologies can improve or modernize it, translate that into a practical engineering approach, and then help turn that approach into a working solution.
- The role therefore requires more than knowledge of individual Google services. It requires an understanding of how applications are designed, developed, migrated, secured, deployed, operated and continuously improved within an enterprise environment.
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.
Note: All official offers from Unilever are issued only via our Applicant Tracking System (ATS). Offers from individuals or unofficial sources may be fraudulent—please verify before proceeding.
How we rate this
Gemini Agent Platform AI Engineer at Unilever rates 67 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.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ 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
Also common in AI engineer jobs at this level
Pay
- Typical AI engineer job
- $182k
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
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through how you've used Gemini in your day-to-day work.
- What are the limits of Vertex AI that you've run into, and how did you work around them?
- Describe a typical day in a role like this one: which parts run through AI directly?
- 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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