At a Glance
- Tasks: Support Google Cloud Sales and Engineering teams in deploying AI/ML technology.
- Company: Join Google Cloud, a leader in AI innovation and technology.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Why this job: Be at the forefront of the AI revolution and make a real impact.
- Qualifications: Bachelor's degree in Computer Science or Data Science; 6 years in AI/ML.
- Other info: Collaborative environment with access to cutting-edge AI resources.
The predicted salary is between 43200 - 72000 £ per year.
Google welcomes people with disabilities. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: London, UK; Munich, Germany; Madrid, Spain; Paris, France; Dublin, Ireland.
Minimum qualifications:
- Bachelor's degree in Computer Science, Data Science, or equivalent practical experience.
- 6 years of experience working in AI/ML as a technical sales engineer or in software engineering.
- Experience in Python and machine learning frameworks (e.g., TensorFlow, PyTorch).
- Experience in Generative AI as a user or a developer.
- Experience delivering technical presentations and leading business value sessions.
Preferred qualifications:
- Experience with full-stack ML engineering to seamlessly combine retrieval-based knowledge and generative text generation to implement and optimize RAG models using first-party and OSS models.
- Experience with implementing search concepts, such as indexing, scoring, relevancy, faceting, and query rewriting and expansion.
- Experience with semantic search frameworks and tools/databases such as LangChain, Faiss, and Pinecone.
- Experience in systems design, with the ability to architect and explain data pipelines, Machine Learning (ML) pipelines, and ML training and serving approaches.
- Understanding of nearest neighbor search concepts.
About The Job:
As a Generative AI Field Solutions Architect, you will support Google Cloud Sales and Engineering teams to incubate, pilot, and deploy Google Cloud’s AI/ML and Generative AI technology with AI native customers, large enterprises, and early-stage AI startups. You will help customers innovate faster with solutions using Google Cloud’s flexible and open infrastructure including AI Accelerators Tensor Processing Unit/Graphics Processing Units (TPU/GPU).
In this role, you will identify, assess, and develop GenAI and AI/ML applications by applying key industry tools, techniques, and methodologies to solve problems. You will help customers leverage accelerators within their overall cloud strategy by helping run benchmarks for existing models, finding opportunities to use accelerators for new models, developing migration paths, and helping to analyze cost to performance. You will work with internal Cloud AI teams to remove roadblocks and shape the future of our offerings.
It’s an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll succeed by leveraging Google’s brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world’s most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
Responsibilities:
- Advise our customers by understanding the customer’s business process and objectives.
- Architect AI-drive, spanning data, AI, and infrastructure, and work with peers to include the full cloud stack into overall architecture.
- Work with customers, demonstrate features, tune models, optimize model performance, profiling, and benchmarking.
- Troubleshoot and find solutions to issues training/serving models in a large-scale environment.
- Build repeatable technical assets such as scripts, templates, reference architectures to enable other customers and internal teams.
- Work cross-functionally to influence Google Cloud strategy and product direction at the intersection of infrastructure and AI/ML by advocating for enterprise customer requirements.
- Coordinate regional field enablement with leadership and work with product and partner organizations on external enablement activities.
- Travel as needed.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
Field Solutions Architect, Applied AI, Google Cloud in London employer: Google
Contact Detail:
Google Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Field Solutions Architect, Applied AI, Google Cloud in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Google Cloud. Use LinkedIn to connect and engage with them. A friendly chat can sometimes lead to opportunities that aren’t even advertised!
✨Tip Number 2
Show off your skills! Prepare a portfolio or a presentation that highlights your experience with AI/ML projects. When you get the chance to meet potential employers, having something tangible to share can really set you apart.
✨Tip Number 3
Practice makes perfect! Get ready for technical interviews by brushing up on your Python and machine learning frameworks. Mock interviews with friends or using online platforms can help you feel more confident when it’s time to shine.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search. So, go ahead and hit that apply button!
We think you need these skills to ace Field Solutions Architect, Applied AI, Google Cloud in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience in AI/ML, Python, and any relevant frameworks like TensorFlow or PyTorch. We want to see how you fit into our world!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a great fit for the Field Solutions Architect role. Let us know what excites you about working with Google Cloud.
Showcase Your Technical Skills: Don’t just list your technical skills—demonstrate them! If you've delivered technical presentations or led business value sessions, share those experiences. We love seeing how you can communicate complex ideas effectively.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets the attention it deserves. Plus, it’s super easy to do—just follow the prompts and let us see what you’ve got!
How to prepare for a job interview at Google
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and the machine learning frameworks mentioned in the job description, like TensorFlow and PyTorch. Brush up on your knowledge of Generative AI and be ready to discuss your hands-on experience with these technologies.
✨Showcase Your Presentation Skills
Since the role involves delivering technical presentations, practice explaining complex concepts in a simple way. Prepare a few examples of past presentations or sessions where you’ve led discussions, focusing on how you conveyed value to your audience.
✨Understand the Customer's Needs
Research common challenges faced by businesses in adopting AI/ML solutions. Be prepared to discuss how you would approach understanding a customer's business process and objectives, and how you can architect solutions that meet their needs.
✨Prepare for Problem-Solving Scenarios
Expect to tackle real-world problems during the interview. Think through potential issues related to training and serving models in large-scale environments, and be ready to share your troubleshooting strategies and solutions from past experiences.