Forward Deployed Engineer - AI , Google Cloud Platform

• 5+ years of Python and REST API engineering
• 2+ years hands-on with AI agentic coding and prompt engineering
• 3+ years across Google Cloud Platform infrastructure and Vertex AI / GECX Suite
• AI and agentic systems: RAG architectures, agentic AI frameworks, AI agent development and coding, prompt engineering, semantic search, ML pipelines, TensorFlow, Scikit-learn, automated AI evaluation frameworks
• Google Cloud Platform and infrastructure: Compute Engine, Cloud Run, Cloud Functions, BigQuery, Vertex AI, Pub/Sub, Kubernetes, Docker, Terraform, IAM
• Languages beyond Python: TypeScript / Node.js, Go, Java / Spring Boot
• Backend and integration: microservices, REST / GraphQL APIs, Kafka, RabbitMQ, Google Cloud Platform Pub/Sub, PostgreSQL, MongoDB, Redis
• Production and SRE: SRE practices, incident response, Root Cause Analysis (RCA), observability and distributed tracing with Datadog, Sentry, New Relic
• Security and compliance: HIPAA, PCI-DSS, RBAC, JWT / API security
• Proven agents you personally built and put into production, not prototypes
• Range to move from an executive conversation to a debugging session the same afternoon

Nice-to-Have

• Advanced RAG technique - re-ranking, hybrid search, context optimization
• Technical workshop facilitation
• Enterprise stakeholder management
• GitHub and JSON data handling
• AI evaluation frameworks and agent testing

• Lead customer-facing technical workshops and surface business and technical requirements before you’re asked for them
• Convert what you hear into agent conversation flows, escalation paths, and defined agent capabilities
• Set acceptance criteria with the customer up front rather than at the end

Build

• Take GECX agents from blank page to full end-to-end implementation
• Write the prompt engineering solutions and RAG pipelines behind them
• Stand up API integrations and the Google Cloud Platform infrastructure they run on
• Build automated evaluation frameworks that demonstrate the agent actually works

Ship and sustain

• Own production deployments, with cutover plans and risks flagged to stakeholders ahead of time
• Watch production systems, chase down issues, run RCA, and see stabilization through
• Explain what broke in language that lands with engineering teams and executives alike

Hand off

• Train customer teams so they can run and maintain their agents without you
• Carry the delivery lifecycle from initial discovery to a stable production footprint

Not a Fit If

• You haven’t personally built and deployed AI agents that run in production
• Your Google Cloud Platform background is theoretical rather than hands-on engineering
• Your AI and agentic development work has stayed at proof-of-concept depth
• Communication and stakeholder-facing work isn’t a strength
• Working directly with enterprise customers and executive stakeholders isn’t something you want

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