Forward Deployed Engineer, AI Expert

Valtech

Forward Deployed Engineer with expertise in Google Cloud and applied AI for enterprise customers. Embedding with teams to code and deploy AI solutions effectively.

Posted 7/13/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $125,000 - $225,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and deploying AI solutions on Google Cloud, with a strong focus on multi-agent systems, Retrieval-Augmented Generation, and integration with enterprise IT infrastructure. Proven ability to lead technical discovery sessions and communicate complex concepts effectively to diverse stakeholders.

Highest-signal resume keywords

Google Cloud Platform (GCP)Python ProgrammingAI System ArchitectureRetrieval-Augmented Generation (RAG)Technical Discovery Sessions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software DevelopmentAI Solution DeploymentMulti-Agent SystemsPrompt EngineeringData Pipeline DevelopmentTerraformVector DatabasesCloud FunctionsBigQueryCloud Storage

Soft Skills

Outstanding Communication SkillsStakeholder Engagement

Tools & Technologies

Vertex AIGemini ModelsCloud RunPub/SubAgent Development Kit (ADK)

Certifications & Qualifications

Bachelor’s Degree in Engineering or Computer Science

Industry Keywords

AI ArchitecturesProduction-Grade CodeEnterprise IT IntegrationAI ProficiencyTechnical Documentation

Tech Stack

Tools & technologies

BigQueryCloudGoogle Cloud PlatformMicroservicesPythonReactTerraformTypeScript

About the role

Key responsibilities & impact

Embed within customer engineering teams and lead technical discovery sessions with business stakeholders, engineering leadership, and security to translate ambiguous business problems into clear AI architectures and delivery plans.
Architect, code, and ship production-grade agentic AI solutions on Google Cloud — including multi-agent systems, MCP servers, sub-agents, skills, connectors, agentic wrappers, and safety guardrails — that move customers beyond pilots into measurable business value.
Design and implement Retrieval-Augmented Generation (RAG) pipelines and grounding architectures, including chunking strategy, vector databases, and embedding optimization to prevent hallucinations and ensure response quality.
Build the “connective tissue” between Google’s AI products and customer infrastructure, including APIs, legacy data silos, identity, and security perimeters.
Implement multi-agent patterns such as ReAct, self-reflection, and hierarchical delegation using frameworks like Google’s Agent Development Kit (ADK) or LangGraph
Build high-performance evaluation pipelines and observability frameworks for agentic systems, with attention to accuracy, safety, latency, cost-per-request, and tokens-per-second.
Debug agent logic and optimize tool selection in live, high-traffic environments, including tracing conversation and request IDs across microservices to resolve production failures.
Co-build with customer engineering teams and act as a hands-on advocate for AI-assisted development, introducing and operationalizing AI coding tools to accelerate delivery and elevate engineering practices.
Drive a deliberate handoff to the customer’s team, ensuring long-term ownership, documentation, and end-user adoption after the engagement concludes.
Develop and maintain technical documentation, architecture decision records, and evaluation results across all assigned engagements.

Requirements

What you’ll need

Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
5+ years of software development experience using Python, TypeScript, or comparable languages, with a track record of shipping production-grade code to external or internal customers.
Hands-on experience architecting and deploying AI systems on Google Cloud Platform (GCP), including:
◦ Vertex AI — model deployment, fine-tuning workflows, evaluation, and platform-level observability.
◦ Gemini models — prompt engineering, structured outputs, function/tool calling, and multimodal use cases.
◦ BigQuery and Cloud Storage — as data and grounding sources for AI workloads.
◦ Cloud Run, Cloud Functions, and Pub/Sub — for deploying and orchestrating agentic services.
◦ Gemini Enterprise Agent Platform — designing, configuring, and deploying enterprise-grade agents, grounding on customer data sources, integrating tools and connectors
Demonstrated experience building agentic and AI-driven solutions in production, including:
◦ LLM application development — prompt engineering, agent development, and evaluation frameworks.
◦ RAG architectures — vector databases, chunking strategy, and retrieval evaluation.
◦ Data pipelines — structured and unstructured data ingestion to power enterprise-grade AI solutions.
Experience deploying cloud resources via Terraform or similar infrastructure-as-code tools.
Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI requirements and translate ambiguous business goals into technical roadmaps.
Experience integrating AI systems with enterprise IT infrastructure, including authenticated APIs, legacy data systems, and corporate security perimeters.
Ability to travel up to 50% of the time to customer sites.
AI proficiency for productivity
Outstanding communication skills, including the ability to explain complex AI and architectural concepts to both deep-technical engineers and non-technical executives.

Benefits

Comp & perks

Flexibility, with remote and hybrid work options (country-dependent)
Career advancement, with international mobility and professional development programs
Learning and development, with access to cutting-edge tools, training and industry experts
Medical, dental, and vision insurance for you and your family, plus employer contributions to Health Savings Accounts