Forward Deployment Engineering (FDE) Lead Generative AI & Google Cloud Platform

Role Overview: We are seeking a senior, client-facing Forward Deployment Engineering (FDE) Lead to embed directly with enterprise customers to build and scale production-ready Generative AI systems. In this high-impact role, you will act as the technical spearhead translating complex client requirements into custom RAG architectures, multi-agent frameworks, and fine-tuned model deployments leveraging Google Cloud Platform Vertex AI, Gemini, GKE, BigQuery, and Vector Search.

Job Summary: FDE Lead (GenAI & Google Cloud Platform Specialist)

• Position Title: Forward Deployment Engineering (FDE) Lead Generative AI & Google Cloud Platform

• Location: Miami, FL

• Employment Type: Full-Time

• Experience Required: 8+ Years Total (3+ Years Dedicated to Production GenAI / LLM Cloud Deployments)

• Google Cloud Platform AI & Data Stack: Vertex AI, Gemini APIs, Vector Search, BigQuery, GKE, Cloud Run, IAM

• LLM & Agentic Ecosystem: RAG Pipelines, LangChain, LlamaIndex, vLLM, Vector DBs (Pinecone, Chroma, pgvector)

• Languages & Infrastructure: Python, TypeScript, Terraform, Docker, MLOps CI/CD

• Preferred Certifications: Google Cloud Platform Professional Cloud Architect or Professional ML Engineer

Key Responsibilities:

• Forward-Deployed Client Engagement: Embed directly with enterprise customer engineering and executive leadership to scope, design, and deploy high-value AI solutions within customer cloud tenancies.

• LLM & RAG Architecture: Design and deploy production-grade LLM workflows, custom RAG architectures, agentic frameworks, and evaluation pipelines using Vertex AI, Gemini, and open-source ecosystems.

• Google Cloud Platform Infrastructure Foundation: Architect secure and resilient cloud foundations to orchestrate AI workloads leveraging GKE, Cloud Run, BigQuery, Vector Search, and IAM.

• Technical Leadership & MLOps: Lead pods of cloud and software engineers, setting standards for code quality, LLM evaluation metrics, token/latency optimization, and enterprise CI/CD (MLOps).

• Client Advisory & Strategy: Partner with C-suite and product leads to build enterprise AI roadmaps, run technical PoC workshops, and solve cloud governance and privacy constraints.

Required Qualifications & Skills:

• 8+ years of software engineering, cloud architecture, or technical consulting experience, with 3+ years dedicated to production AI/LLM applications.

• Mandatory: Deep hands-on mastery of Vertex AI, Gemini APIs, Google Cloud Platform Vector Search, and core Google Cloud Platform infrastructure (GKE, Cloud Run, BigQuery).

• Fluency in Python and/or TypeScript along with Terraform for Infrastructure as Code.

• Practical experience with RAG pipelines, embedding models, vector databases (Pinecone, Chroma, pgvector), and orchestration frameworks (LangChain, LlamaIndex, vLLM).

• Hands-on MLOps/DevOps experience deploying containerized workloads (Docker, Kubernetes/GKE) and managing model performance, latency, and cost tuning.

• Exceptional consultative and client-facing technical leadership skills.

Preferred Qualifications:

• Active certifications: Google Cloud Platform Professional Cloud Architect or Google Cloud Platform Professional Machine Learning Engineer.

• Experience fine-tuning open-weights models (e.g., Llama, Gemma), building custom guardrails, or leading pre-sales PoC discoveries.