Customer Engineer - AI/ML

Overview

As a Google AI/ML Customer Engineer you will play an integral part to the pre-sales customer journey.  Your role will be to help uncover technical pain points that align Woolpert Services and Google technologies.  This will be done by first listening to the customer and then technically qualifying the opportunity through a series workshops, demonstrations, white boarding and ideations sessions.  All of our customers are unique, this role will require someone who can relate with our customers, be creative and position all opportunities as a Win-Win for the customer, Woolpert and Google

Responsibilities

• Be a trusted advisor to customers, helping them understand and incorporate AI accelerators into their overall cloud strategy by recommending migration paths, integration strategies, and application architecture that incorporate Google Cloud AI optimized infrastructure.
• Demonstrate how Google Cloud is differentiated, highlighting the power of accelerators by working with customers on proof-of-concepts, demonstrating features, optimizing model performance, profiling, and bench-marking.
• Influence Google Cloud strategy at the intersection of infrastructure and AI/ML by advocating for enterprise customer requirements.
• Travel to customer sites and events as needed.
• Be responsible for business growth and workload acceleration on AI infrastructure products and solutions for GCP.

• Pre-Sales Support (80%)
  • Engage with customers to understand their pain points.
  • Advise customers on best practices for deploying and managing applications in cloud environments (GCP, AWS, Azure, and on-premises).
  • Technically qualify opportunities.
  • Architect complex LBS solutions for customer environments (on-premises, cloud, or hybrid).
  • Collaborate with the sales team to identify customer needs and propose appropriate solutions.
  • Collaborate with Sales, Customer Engineering, and Engineering to ensure solution fit, feasibility, and profitability.
  • Conduct product demonstrations, workshops, and presentations to showcase the value of Google Maps and Google Cloud Geospatial technologies.
  • Ability to create estimated Total Cost of Ownership (TCO) and Return On Investment (ROI) calculations for Customers.
  • Create and deliver Statements of Work (SOWs).
  • Experience with Data Pipelines
  • Experience LLM Model development Vertex
  • Experience with Consumer Grade Models
  • Application Development Experience

• AI/ML Technical Leadership (20%)
  • Maintain expert-level knowledge of Google AI/ML technologies and the challenges they address for customers.
  • Possess in-depth knowledge of current and future Google Maps and Google Cloud Geospatial licensing models.
  • Stay informed about technology updates, licensing changes, and business trends that could impact Woolpert's go-to-market strategy by networking within Google and with key partners.
  • Design, create, and continuously update demos and sales collateral (e.g., reference architectures, SOW templates) to empower Sales and Customer Engineering teams to accelerate sales cycles.
  • Design repeatable sales plays and sales motions.
  • Train Sales and Customer Engineering teams on current and future go-to-market strategies and available sales collateral.
  • Collaborate closely with internal teams (sales, engineering, support) to ensure seamless service delivery.
  • Become the voice of Woolpert Digital Innovations' LBS strategy by creating and maintaining a digital presence (social media posts, blog articles, video shorts).
  • Actively participate in the AI/ML community through speaking engagements and other outreach activities.
  • Obtain and maintain all advanced-level AI/ML certifications.
  • Participate in projects involving AI/ML technologies.

 

Qualifications

• Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
• 10 years of experience with cloud native architecture in a customer-facing or support role.
• 5 years of experience with cloud infrastructure.
• 5 years of experience in a technical role focused on AI infrastructure or related areas
• Experience building and operationalizing machine learning models.
• Experience with GPU programming (e.g., CUDA, OpenCL) and optimization techniques.
• Experience with high-performance computing (HPC) environments and contributions to open-source projects related to AI or infrastructure.
• Experience training and fine-tuning large models (e.g., image, language, segmentation, recommendation, genomics) with accelerators.
• Experience with performance profiling tools (e.g., TensorFl