Senior Forward Deployed Engineer, Gemini Enterprise Platform

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🕒 vor 1 Monat

🇺🇸 Vereinigte Staaten – Remote

⏰ Vollzeit

🟠 Senior

👷🏻‍♀️ Ingenieur

👻 Geisterscore 24%

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AuxoAI

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

Gegründet 2022

🤖 Künstliche Intelligenz

☁️ SaaS

🤝 B2B

Artificial Intelligence • SaaS • B2B

AuxoAI ist ein auf Unternehmenskunden spezialisiertes AI-Unternehmen, das platform- und produktbasierte AI-Lösungen für Go-to-Market, Sales Acceleration, Market Simulation und Data Modernization entwickelt. Das Angebot umfasst AI Core (Platform), Echo für Market Simulation & Intelligence, Kimavi für Sales Acceleration, Damia sowie Lösungssuites wie DataNext für Data Modernization, AINext für individuelle AI-Entwicklung und MarketNext für GTM Acceleration. AuxoAI hat seinen Hauptsitz in der San Francisco Bay Area und Niederlassungen in Indien. Das Unternehmen richtet sich mit AI-Produkten im SaaS-Modell und Professional Services an Geschäftskunden.

Beschreibung

• Build agents ground-up in ADK and by forking and hardening Agent Garden templates, defining instructions, model selection, tools, orchestration, grounding and memory • Select and bind models per agent or per step for cost and latency; implement structured output, thinking-level and safety configuration • Run evaluation and simulation before ship using trajectory and response metrics and synthetic-user simulation; act on Agent Optimizer findings • Build MCP servers to expose client systems and data as agent tools; integrate third-party MCP servers; wire OpenAPI and Google Cloud toolsets • Implement multi-agent (A2A) hand-offs where required • Build context-graph foundations on BigQuery graph and/or Spanner Graph, plus retrieval and grounding paths using Vertex AI, Vector Search, Embeddings and RAG • Build and operate supporting data stacks including BigQuery models, Dataform pipelines, Dataproc jobs and Pub/Sub streams, with cataloguing, lineage and classification in Dataplex Universal Catalog / Knowledge Catalog • Deploy agents to Agent Engine, Cloud Run or GKE via the Agents CLI and infrastructure-as-code • Instrument observability with Cloud Trace / OpenTelemetry and apply governance using Model Armor, Semantic Governance and Agent Identity • Publish agents into the client's Gemini Enterprise app catalog and configure Google Workspace integration • Pair with client engineers and leave them able to maintain and extend the delivered solution • Provide pre-sales support through proofs of concept, demos and effort inputs

🎯 Anforderungen

• Master's or Bachelor's degree in Computer Science, Engineering or a related field, or equivalent practical experience • 6+ years building and shipping production software or data / ML systems, with strong Python • Hands-on experience building LLM agents with a code-first framework (ADK preferred; LangGraph, CrewAI, LlamaIndex or Amazon Bedrock Agents accepted), including tools, retrieval grounding and evaluation • Strong BigQuery and SQL, and hands-on experience with at least one graph store (Spanner Graph, BigQuery graph, Neo4j or equivalent) • Built at least one data pipeline in production (Dataform, Dataproc / Spark, dbt or equivalent) and worked with a streaming / eventing system (Pub/Sub or equivalent) • Deployed services to a managed or container runtime (Cloud Run, GKE, Kubernetes or equivalent) with infrastructure-as-code (Terraform) • Client-facing or embedded delivery experience; able to pair with a client's engineers and hand over cleanly • Google Cloud Professional certification (Data Engineer, Machine Learning Engineer, or Cloud Developer) (preferred) • Hands-on with the Gemini Enterprise Agent Platform — ADK, Agent Garden, Model Garden, Agent Engine, Agent Studio, Agents CLI (preferred) • Built or operated MCP servers, and integrated third-party MCP servers into an agent (preferred) • Built a retrieval / grounding layer over a knowledge or context graph (preferred) • Experience with Gemini Enterprise app publishing and Google Workspace integration (preferred) • Experience with agent evaluation and observability at production scale (autoraters, trajectory metrics, Cloud Trace) (preferred)

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