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- Tasks: Support Google Cloud Sales by deploying AI/ML technology for innovative customers.
- Company: Join Google, a leader in tech innovation and inclusivity.
- Benefits: Competitive salary, diverse work culture, and opportunities for growth.
- Why this job: Shape the future of AI with cutting-edge technology and impactful projects.
- Qualifications: Experience in AI/ML, Python, and technical sales engineering required.
- Other info: Work in a dynamic environment with global collaboration opportunities.
The predicted salary is between 36000 - 60000 £ per year.
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 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 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 such as 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 running benchmarks for existing models, finding opportunities to use accelerators for new models, developing migration paths, and analyzing cost to. You will work with internal Cloud AI teams to remove roadblocks and shape the future of our offerings.
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.
Field Solutions Architect, Applied AI, Google Cloud in London employer: Google Inc.
Contact Detail:
Google Inc. 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. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio or case studies showcasing your AI/ML projects. When you get the chance to chat with recruiters or hiring managers, let your work do the talking.
✨Tip Number 3
Practice makes perfect! Get ready for technical interviews by brushing up on your Python and ML frameworks. Mock interviews with friends or using online platforms can help you feel more confident.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining the Google Cloud team.
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 is tailored to the Field Solutions Architect role. Highlight your experience in AI/ML, Python, and any relevant frameworks like TensorFlow or PyTorch. We want to see how your skills align with what we're looking for!
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 Google Cloud. Let us know what excites you about this role and our mission.
Showcase Your Technical Skills: Don’t forget to showcase your technical skills in your application. Mention any experience with ML engineering, data pipelines, or semantic search frameworks. We love seeing candidates who can demonstrate their expertise!
Apply Through Our Website: We encourage you to apply through our website for the best chance of being noticed. It’s the easiest way for us to keep track of your application and ensure it gets to the right people. Good luck!
How to prepare for a job interview at Google Inc.
✨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 understanding of full-stack ML engineering and be ready to discuss how you’ve implemented RAG models or similar projects.
✨Showcase Your Presentation Skills
Since delivering technical presentations is key for this role, practice explaining complex concepts in a simple way. Prepare a mini-presentation on a relevant topic, perhaps about a project you've worked on, to demonstrate your ability to communicate effectively with both technical and non-technical audiences.
✨Understand the Customer's Needs
Research common challenges faced by AI native customers and large enterprises. Be prepared to discuss how you would approach understanding their business processes and objectives, and think of examples where you’ve successfully advised clients in the past.
✨Prepare for Problem-Solving Questions
Expect to tackle some technical problem-solving scenarios during the interview. Brush up on troubleshooting model training and serving issues, and think through how you would architect solutions that span data, AI, and infrastructure. Practising these scenarios can help you feel more confident.
