hackajob
Forward Deployed Engineer, Applied AI, Google Cloud
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
hackajob is collaborating with Google to connect them with exceptional professionals for this role.
As a Forward Deployed Engineer (FDE) in Applied AI, you are the "Agent Engineer" and the primary driver for our customers' most critical AI initiatives. You take initial conversational prototypes and transform them into production-ready solutions, owning the end-to-end engineering life-cycle, including the transition from "Art-of-the-Possible" to real-world business value and scalable, secure AI systems. In this high-travel, high-impact role, you will be focused on leading technical delivery for Conversational AI pilots and establishing the first Customer User Journeys (CUJs) for our largest customers at their sites. You will require a deep understanding of software engineering, machine learning operations, and cloud infrastructure.
It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll leverage 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.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Minimum Qualifications
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 8 years of experience with software development using Python or similar coding languages.
- Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP).
- Experience deploying resources using Terraform or similar tools, to automate the setup of agents, functions, or networking.
- Experience building full-stack applications that interact with enterprise IT infrastructures, and developing external customer projects.
Preferred Qualifications
- Master’s or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks like ReAct and self-reflection.
- Experience debugging Agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.
- Experience connecting agents to enterprise knowledge bases and optimizing Retrieval-augmented generation (RAG) chunking to prevent hallucinations.
- Ability to travel up to 50% of the time.
- Track record of troubleshooting live, high-traffic systems during critical windows.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Responsibilities
- Serve as the lead developer for complex Conversational AI and CX applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable return on investment.
- Architect and code conversational flows that are not just functional, but optimized for the "connective tissue" between Google’s Conversational AI products and customers’ live infrastructure, including APIs, legacy data silos, and security perimeters.
- Build high-performance evaluation (Eval) pipelines and observability frameworks to optimize complex agentic workloads, focusing on reasoning loops, tool selection, and reducing latency while maintaining production-grade security and networking.
- Identify repeatable field patterns and technical "friction points" in Google’s AAI stack, converting them into reusable modules or product feature requests for Engineering teams.
- Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location