only 15+ yrs profiles Forward Deployment Engineer — GCP AI/ML Consulting at Remote
Priyanka S
Forward Deployment Engineer — GCP AI/ML Consulting · |
DEPARTMENT Consulting | LEVEL Senior | LOCATION Remote | TYPE 40 hrs |
About the Role
As a GCP Forward Deployment Engineer (FDE), you will sit at the intersection of applied AI engineering and hands-on customer partnership. You will embed directly with our most strategic enterprise customers to design, prototype, and deliver production-grade AI solutions on Google Cloud — building agentic systems with Gemini and the Agent Development Kit (ADK), writing expert-level Python, and moving fast enough to unblock customers in hours, not weeks. This is not a pre-sales or support role: you are an L3-caliber software engineer and AI practitioner who works at the frontier of what GCP makes possible, turning customer problems into intelligent, scalable solutions.
Core Competencies
Every GCP FDE is expected to demonstrate mastery across four foundational competencies:
AI Engineering Deep expertise in AI and machine learning systems | Full-Stack Delivery End-to-end solutions from front-end through back-end | Rapid Prototyping Build and iterate on proofs of concept with speed | Customer-Centric Translate customer needs into precise technical solutions |
What You'll Do
• Embed with enterprise customers to scope, architect, and deliver end-to-end AI solutions on Google Cloud — from data layer through front-end interface
• Design and build agentic AI systems using Gemini, the Google Agent Development Kit (ADK), and LangChain — including multi-agent orchestration, tool use, memory, and grounding
• Write expert-level Python to develop, train, evaluate, and deploy ML models and AI pipelines on Vertex AI
• Prototype rapidly — solve customer-blocking technical scenarios (e.g., building a custom audio connector or data integration) within hours when required
• Build full-stack solutions spanning BigQuery, Cloud Run, Cloud Functions, Pub/Sub, Apigee, and Vertex AI to deliver integrated, production-ready systems
• Lead technical discovery sessions to identify high-value AI use cases, assess data readiness, and define measurable success criteria with customers
• Implement MLOps best practices on Vertex AI: automated pipelines, model monitoring, feature stores, and CI/CD for ML workflows
• Leverage Google AI services — Document AI, Speech-to-Text, Vision AI, Translation AI — for rapid capability delivery where custom models are not required
• Advise customers on responsible AI, model explainability, and AI governance using Google Cloud's built-in tooling
• Feed customer insights back to Fusion's engineering and product teams to shape our GCP AI practice
• Produce clear architecture diagrams, technical runbooks, and handoff documentation for every engagement
• Travel to customer sites as needed (typically up to 30%)
What We're Looking For
Required Qualifications
• L3 SWE Proficiency: Expert-level Python coding ability, including software design patterns, performance optimization, testing, and production-grade code quality
• Agentic Fluency: Hands-on experience building agents using Gemini, Google ADK, and/or LangChain — including tool use, agent memory, multi-agent workflows, and RAG pipelines
• Rapid Prototyping: Demonstrated ability to solve ambiguous, blocking customer scenarios quickly — building working prototypes (e.g., audio connectors, API integrations, data pipelines) in hours
• Full-Stack Delivery: Experience delivering end-to-end solutions across the stack, from data ingestion and model inference through APIs and user-facing interfaces
• 5+ years of professional software engineering experience, with at least 1 year deploying AI/ML solutions on GCP or equivalent cloud platform
• Hands-on experience with Vertex AI: training jobs, model endpoints, Pipelines, Feature Store, and/or Model Monitoring
• Strong understanding of GCP data services: BigQuery, Dataflow, Pub/Sub, and Cloud Storage
• Familiarity with GCP infrastructure fundamentals: IAM, VPC, Cloud Run, Cloud Functions, and Cloud Logging
• Excellent communication skills — able to present complex AI system behavior and trade-offs to both technical teams and executive stakeholders
Nice to Have
• Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect certification
• Experience with Gemini multimodal capabilities — vision, audio, and document understanding
• Background in NLP, computer vision, conversational AI, or time-series forecasting in a production setting
• Familiarity with additional agentic frameworks: CrewAI, AutoGen, or Google's Vertex AI Agent Builder
• Experience with Looker, Looker Studio, or data visualization for AI-driven analytics products
• Prior customer-facing experience in an applied AI or ML engineering role (e.g., Google PSO, ML consulting, or AI professional services)
• Knowledge of responsible AI practices: fairness metrics, model explainability (SHAP, LIME), and AI governance frameworks
What Success Looks Like
In your first 30 days, you'll shadow existing GCP customer engagements, get hands-on with our AI delivery methodology, and build your first Gemini-powered agent or Vertex AI pipeline. Within 90 days, you'll be leading your own engagements end-to-end — from use case discovery through full-stack deployment. Within 6 months, you'll be the go-to technical authority on agentic AI delivery at Fusion, influencing how we build our GCP practice, and setting the bar for what rapid, high-quality AI deployment looks like for our customers.
Why This Role
• Frontier work — you'll build with Gemini, ADK, and the latest GCP AI capabilities as they emerge, often before the broader market
• Speed — unlike traditional consulting, you ship working AI in days, not quarters
• Full-stack impact — you own the entire solution, from model to interface, with direct customer visibility
• GCP partnership — close collaboration with Google Cloud engineering and account teams, with access to early-access programs and model previews
• Ownership — you run engagements end-to-end with high autonomy and direct relationships with customer technical leadership
• Career growth — a natural path toward AI Architect, GCP Practice Lead, or Head of AI Engineering roles
