Forward Deployed Engineer, Generative AI, Google Cloud

Forward Deployed Engineer, Generative AI, Google Cloud corporate_fare Google place Addison, TX, USA ; Washington

D.C., DC, USA

bar_chart Advanced Advanced Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain. info_outline

X Note:

By applying to this position you will have an opportunity to your preferred working location from the following: Addison, TX, USA; Washington

D.C., DC, USA

.

Minimum qualifications:

Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience. 8 years of experience in Python or other programming languages in machine learning (e.g., Java, C++, Go). Experience in a client-facing technical role, managing or deploying solutions for customers in the telecommunications industry. Experience in applied AI, with a focus on designing and evaluating systems around foundation models (e.g., prompt engineering, fine-tuning, Retrieval-augmented generation (RAG), orchestrating model interactions with external tools to deliver solutions). Experience architecting, deploying, or managing solutions on a cloud platform.

Preferred qualifications:

Master's degree in Computer Science, Engineering, or a related technical field. Experience delivering AI solutions specifically for Telecommunications use cases, such as network optimization, churn prediction, or customer experience enhancement. Experience with distributed training and optimizing performance versus costs. Experience training and fine tuning models in large scale environments with accelerators. Experience in systems design with the ability to architect and explain data pipelines, ML pipelines, and ML training and serving approaches. Understanding of the founder or startup CTO mindset, with a bias for action and applying product insights to solve immediate customer challenges and unlock long-term value. About the job Your primary responsibility will be to construct rapid prototype Generative AI applications tailored to Google Cloud customers, catering to various clientele ranging from early stage startups to prominent, established companies. You will be a hybrid professional, blending the core competencies of an engineer with an aptitude for customer engagement and strategic problem-solving. Your role will require you to lead with deep, bespoke implementation as the primary value proposition, ensuring that core technology delivers demonstrable value in the customer's unique operational context. You will have close collaboration with product and engineering teams to eliminate obstacles and shape the future trajectory of offerings. You will be adept at disseminating lessons learned to customers and internal Google teams, translating one-off customer solutions into reusable, scalable assets. The US base salary range for this full-time position is $183,000-$265,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about . Responsibilities Act as a trusted advisor to customers by understanding their business process and objectives, designing and building end-to-end GenAI-driven solutions spanning AI, Data, and Infrastructure, while partnering with peers to incorporate the full cloud stack into overall architecture. Develop production-grade prototypes that deliver measurable outcomes, including writing custom code, integrating disparate data sources, designing data ontologies, deploying solutions on customer infrastructure. Represent the customer, gathering real-time feedback and insights, formalizing and abstracting field-tested solutions into reusable modules or new product features to drive product innovation, and establishing technical and business cases to support recommendations. Drive cross-functional influence over Google Cloud strategy and product direction at the intersection of infrastructure and AI/ML by advocating for customer requirements. Lead regional field enablement in collaboration with leadership and engage with product and partner organizations on external enablements, with travel as needed.