Forward Deployed Engineer (FDE) IRC306872

About the Role

A hybrid hands-on and customer-facing role for a Principal / Lead Cloud AI Solutions Engineer with deep expertise in Google Cloud Platform (GCP) and modern Enterprise AI paradigms. You will lead the architecture, design, and deployment of scalable microservices, distributed systems, and cutting-edge GenAI/Agentic solutions. You will own the complete solution lifecycle—from direct client discovery and architectural design to hands-on implementation and production deployment—translating complex, ambiguous business problems into secure, high-performance Cloud AI systems.

Key Responsibilities

• Architecture & System Design: Architect and implement resilient, scalable, and high-throughput enterprise systems and microservices using GCP-native cloud frameworks.
• GenAI & Agentic Engineering: Build and deploy enterprise GenAI applications leveraging Vertex AI, Gemini API, Vertex AI Vector Search, RAG pipelines, and agentic AI workflows. Establish automated evaluation loops (“evals”) to measure model performance, drift, and accuracy.
• Full-Lifecycle Engineering: Lead end-to-end delivery—including technical requirement gathering, architectural blueprints, Proofs of Concept (PoCs), production integration, deployment, and performance optimization.
• Cloud & DevOps Orchestration: Implement robust CI/CD pipelines and manage containerized microservices across Google Cloud Run, GKE, Cloud Functions, Pub/Sub, and BigQuery using Docker and Kubernetes.
• Customer & Stakeholder Engagement: Serve as the technical authority in direct client engagements, translating business requirements into actionable engineering roadmaps and conducting technical deep dives.
• Technical Leadership: Guide engineering teams through design and code reviews, mentor junior engineers, establish best practices, and drive technical excellence across cross-functional initiatives.

Requirements & Tech Stack

• Core Development Track (Python or Java)
• Strong, hands-on enterprise-level expertise in EITHER Python OR Java.
• Proven track record building enterprise microservices, REST APIs, and distributed event-driven applications.
• Technical & Cloud Architecture
• Deep technical knowledge of distributed system design, enterprise application integration, and relational/non-relational database technologies.
• Advanced expertise in Google Cloud Platform (GCP) core services: Google Cloud Run, GKE, Cloud Functions, BigQuery, and Pub/Sub.
• Production experience with Docker, Kubernetes, infrastructure-as-code, and enterprise CI/CD automation pipelines.
• GCP AI & Machine Learning Ecosystem
• Hands-on proficiency in building and deploying models via Vertex AI and integrating foundation models via the Gemini API.
• Demonstrated experience constructing end-to-end Retrieval-Augmented Generation (RAG) systems and configuring Vertex AI Vector Search for embeddings and semantic search.
• Practical experience designing agentic AI workflows and establishing automated evaluation metrics (“evals”) for enterprise AI applications.
• Client & Leadership Capabilities
• Experience in customer-facing roles, driving technical discovery, solutioning, and executive-level technical discussions.
• Strong problem-solving, debugging, and analytical skills with a track record of optimizing application performance.
• Exceptional communication and interpersonal skills, with the ability to bridge the gap between technical complexity and business strategy.

Qualifications

• GCP Professional Cloud Architect or GCP Professional Machine Learning Engineer certification.
• Experience in regulated industries (FinTech, Healthcare, Enterprise SaaS) with strict security and compliance considerations.
• Knowledge of FinOps and cloud cost-optimization strategies for large-scale GenAI workloads.