Forward Deployed Engineer - AI , Google Cloud Platform

The Opportunity

An enterprise AI delivery team is adding a Forward Deployed Engineer who stays with the customer across the full arc of a build - first conversation through stabilized production. You'll turn business problems into working AI agents, RAG pipelines, integrations, and the Google Cloud Platform architecture underneath them. Customer contact is constant: a workshop one day, a root cause analysis the next, an executive readout in between. Coding stays hands-on throughout.

What You Bring

Must-Have

• 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

What You'll Do

Discovery and design

• 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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