{"schemaVersion":"jobsearcher.job.v1","id":"e443f4f2d8e0756f8dd67b96","url":"https://jobsearcher.com/jobs/e443f4f2d8e0756f8dd67b96","canonicalUrl":"https://jobsearcher.com/jobs/e443f4f2d8e0756f8dd67b96","title":"Customer Solution Architect — Arango AI Product Suite","description":"Customer Solution Architect — Arango AI Product Suite\n\nAbout ArangoDB\n\nArango makes your business data AI-ready, giving agents, apps, and assistants trusted context at scale. Every answer is traceable. Every decision is governed. No more stitching together a vector store, a graph database, a search index, and a governance layer added as an afterthought. Arango’s Contextual Data Platform has it all built in, not bolted on. Trusted by organizations including NVIDIA, HPE, Zscaler, the London Stock Exchange, the U.S. Air Force, NIH, Siemens, and Articul8, Arango helps enterprises move from AI pilots to reliable production systems faster while lowering infrastructure complexity and total cost of ownership. Arango is a proud member of the NVIDIA Inception Program and the AWS ISV Accelerate Program. Stop building Frankenstacks. Start building with Arango. Learn more at arango.ai. We believe great innovation happens when curious, driven people collaborate. We are committed to building a diverse and inclusive team and supporting our employees and interns as they learn, grow, and contribute to shaping the future of enterprise AI.\n\nAbout the role\n\nArango is hiring a Customer Solution Architect to be the primary professional services interface between Arango and the customers deploying our AI product suite. You own the technical relationship end to end, from first discovery through production and expansion. Your job is to turn a customer's problem into a working architecture on Arango's multi-model platform and its GraphRAG and knowledge-graph capabilities, prove value early, and guide the customer's team through deployment and adoption. The role sits where solution architecture, graph data modeling, and applied AI meet. It suits someone who can hold a design conversation with a customer's chief architect in the morning and review a GraphRAG retrieval design with their engineers in the afternoon. Deep graph expertise is not optional here. It is the core of how Arango's AI suite delivers value, and the CSA is expected to be the customer's most trusted source of graph and GraphRAG design judgment.\n\nKey responsibilities\nOwn the technical customer relationship as the primary professional services contact across the full lifecycle: discovery, design, pilot, production, and expansion.\nRun discovery with customer sponsors, domain experts, and operators to identify high-value use cases for Arango's AI product suite, and qualify them against real business outcomes.\nDesign target architectures on Arango's multi-model platform, including graph data models, AQL query and traversal patterns, and GraphRAG retrieval design tailored to the customer's domain.\nDefine success criteria, SLAs/SLOs, data access and governance requirements, and a phased delivery plan from proof of value to production.\nBuild reference implementations and prototypes that prove value early: graph schema, data connectors, GraphRAG pipelines, tool and agent orchestration, APIs.\nGuide production deployment into secure, observable services alongside the customer's engineers, with CI/CD, infrastructure-as-code, and proper testing.\nArchitect retrieval across graph traversal, vector search, and hybrid approaches (chunking, embeddings, ranking, caching), and orchestrate tool and agent calls.\nEstablish evaluation practices and iterate on prompts, models, retrieval strategy, and graph structure using offline and online metrics and A/B tests.\nDesign data pipelines (ETL/ELT), vector indices, graph ingestion, and metadata governance.\nDefine monitoring for quality, drift, hallucination and guardrail events, latency, and cost, and stand up alerting and dashboards with the customer.\nArchitect role-based access, secrets management, audit logging, PII redaction, and content safety controls.\nMeet customer compliance requirements (SOC 2/ISO 27001, GDPR/CCPA, HIPAA as applicable).\nProduce architecture documentation, runbooks, and reusable patterns, and train customer engineers and end users.\nAct as the voice of the customer to Arango's product and engineering teams, shaping the roadmap with what we learn in the field.\nRequired qualifications\nDeep graph knowledge (central to this role). Hands-on expertise in graph data modeling, graph query and traversal (AQL, or equivalents such as Cypher or Gremlin), graph algorithms, and knowledge-graph design for AI. Direct experience building GraphRAG or knowledge-graph-backed retrieval for LLM applications.\n5+ years in software engineering, solution architecture, or technical professional services, including building and operating production systems.\nStrong applied AI and Python skills, with a solid grasp of data structures, systems design, concurrency, and networking.\nStrong database skills across graph, NoSQL, key-value, and document models. Multi-model experience is valued given Arango's platform.\nHands-on experience with modern LLMs and tooling (OpenAI/Anthropic/Llama, Hugging Face, LangChain/LlamaIndex, function and tool calling).\nRetrieval and vector databases (FAISS, pgvector, Pinecone, Weaviate, or similar), and hybrid retrieval that combines graph and vector.\nCloud and containers (AWS/GCP/Azure), Docker/Kubernetes, IaC (Terraform/CloudFormation), and CI/CD.\nObservability (metrics, logs, traces) and performance tuning for latency-sensitive services.\nExcellent customer-facing communication, with the ability to lead technical conversations from the executive level down to the engineering team.\nLocation: Remote\n\nNice to have\nDirect ArangoDB experience, or prior work deploying a graph database in production.\nSearch and IR fundamentals (BM25, hybrid retrieval, re-ranking, ColBERT, cross-encoders).\nFront-end or full-stack experience (TypeScript/React, Next.js) for light UI prototyping.\nMLOps platforms and evaluation frameworks (MLflow, Weights & Biases, Ragas, promptfoo, DeepEval).\nModel adaptation and inference optimization awareness (LoRA/PEFT, DPO, distillation, quantization, vLLM/TGI/TensorRT-LLM), enough to advise on tradeoffs rather than to hand-build.\nDomain experience in finance, healthcare, public sector, manufacturing, or retail.\nSecurity and compliance familiarity: data residency, KMS/HSM, private networking.\nFrench government or industry experience.\nWhat success looks like (6–12 months)\n2 to 4 customer deployments of Arango's AI suite live in production against agreed uptime, latency, and cost targets.\nMeasurable quality and business outcomes (task accuracy, deflection rate, cycle time) backed by evaluation and telemetry.\nReusable graph and GraphRAG reference architectures and connectors adopted by the broader delivery team and by customers.\nCustomer teams enabled and self-sufficient, with runbooks, documentation, and training in place, and strong satisfaction and NPS.\nA credible field feedback loop feeding Arango's product and engineering roadmap.\nOur toolset\nPlatform & Graph: ArangoDB multi-model (graph, document, key-value), AQL, graph algorithms, GraphRAG\nModels & SDKs: OpenAI, Anthropic, Meta Llama, Hugging Face\nRetrieval: graph traversal plus FAISS, pgvector, Pinecone, Weaviate; rerankers (ColBERT, cross-encoders)\nPipelines & Orchestration: LangChain, LlamaIndex, Ray, Airflow\nMLOps & Evals: MLflow, Weights & Biases, Ragas, promptfoo, Great Expectations\nServing & Infra: vLLM, TGI, FastAPI/gRPC, Docker/K8s, Terraform, GitHub Actions\nObservability & Guardrails: OpenTelemetry, Prometheus/Grafana, Llama Guard/Content Safety, custom filters\nData: Postgres/BigQuery/Snowflake; Kafka; object storage\nWhat Makes Arango Special?\nAt Arango, we believe that AI is only as powerful as the data foundation. Our mission is to help organizations build AI systems that can reason, decide and act based on unified, current, and trusted business context at scale. We are helping define a new category of infrastructure: the contextual data layer for AI.\nWorking at Arango means:\nContributing to cutting-edge AI and data infrastructure\nCollaborating with experienced engineers, marketers, and product leaders\nHelping shape how enterprises build AI-powered applications\nIf you're excited about the intersection of AI, data, and social media, we’d love to hear from you.\nY0kjBR4kLh","company":"Arango","rawCompany":"arango","city":"Remote","state":"OR","isRemote":false,"isActive":false,"createdAt":"2026-08-06T15:07:07.121Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1211.00","title":"Computer Systems Analysts","slug":"computer-systems-analysts"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Customer Solution Architect — Arango AI Product Suite","description":"Customer Solution Architect — Arango AI Product Suite\n\nAbout ArangoDB\n\nArango makes your business data AI-ready, giving agents, apps, and assistants trusted context at scale. Every answer is traceable. Every decision is governed. No more stitching together a vector store, a graph database, a search index, and a governance layer added as an afterthought. Arango’s Contextual Data Platform has it all built in, not bolted on. Trusted by organizations including NVIDIA, HPE, Zscaler, the London Stock Exchange, the U.S. Air Force, NIH, Siemens, and Articul8, Arango helps enterprises move from AI pilots to reliable production systems faster while lowering infrastructure complexity and total cost of ownership. Arango is a proud member of the NVIDIA Inception Program and the AWS ISV Accelerate Program. Stop building Frankenstacks. Start building with Arango. Learn more at arango.ai. We believe great innovation happens when curious, driven people collaborate. We are committed to building a diverse and inclusive team and supporting our employees and interns as they learn, grow, and contribute to shaping the future of enterprise AI.\n\nAbout the role\n\nArango is hiring a Customer Solution Architect to be the primary professional services interface between Arango and the customers deploying our AI product suite. You own the technical relationship end to end, from first discovery through production and expansion. Your job is to turn a customer's problem into a working architecture on Arango's multi-model platform and its GraphRAG and knowledge-graph capabilities, prove value early, and guide the customer's team through deployment and adoption. The role sits where solution architecture, graph data modeling, and applied AI meet. It suits someone who can hold a design conversation with a customer's chief architect in the morning and review a GraphRAG retrieval design with their engineers in the afternoon. Deep graph expertise is not optional here. It is the core of how Arango's AI suite delivers value, and the CSA is expected to be the customer's most trusted source of graph and GraphRAG design judgment.\n\nKey responsibilities\nOwn the technical customer relationship as the primary professional services contact across the full lifecycle: discovery, design, pilot, production, and expansion.\nRun discovery with customer sponsors, domain experts, and operators to identify high-value use cases for Arango's AI product suite, and qualify them against real business outcomes.\nDesign target architectures on Arango's multi-model platform, including graph data models, AQL query and traversal patterns, and GraphRAG retrieval design tailored to the customer's domain.\nDefine success criteria, SLAs/SLOs, data access and governance requirements, and a phased delivery plan from proof of value to production.\nBuild reference implementations and prototypes that prove value early: graph schema, data connectors, GraphRAG pipelines, tool and agent orchestration, APIs.\nGuide production deployment into secure, observable services alongside the customer's engineers, with CI/CD, infrastructure-as-code, and proper testing.\nArchitect retrieval across graph traversal, vector search, and hybrid approaches (chunking, embeddings, ranking, caching), and orchestrate tool and agent calls.\nEstablish evaluation practices and iterate on prompts, models, retrieval strategy, and graph structure using offline and online metrics and A/B tests.\nDesign data pipelines (ETL/ELT), vector indices, graph ingestion, and metadata governance.\nDefine monitoring for quality, drift, hallucination and guardrail events, latency, and cost, and stand up alerting and dashboards with the customer.\nArchitect role-based access, secrets management, audit logging, PII redaction, and content safety controls.\nMeet customer compliance requirements (SOC 2/ISO 27001, GDPR/CCPA, HIPAA as applicable).\nProduce architecture documentation, runbooks, and reusable patterns, and train customer engineers and end users.\nAct as the voice of the customer to Arango's product and engineering teams, shaping the roadmap with what we learn in the field.\nRequired qualifications\nDeep graph knowledge (central to this role). Hands-on expertise in graph data modeling, graph query and traversal (AQL, or equivalents such as Cypher or Gremlin), graph algorithms, and knowledge-graph design for AI. Direct experience building GraphRAG or knowledge-graph-backed retrieval for LLM applications.\n5+ years in software engineering, solution architecture, or technical professional services, including building and operating production systems.\nStrong applied AI and Python skills, with a solid grasp of data structures, systems design, concurrency, and networking.\nStrong database skills across graph, NoSQL, key-value, and document models. Multi-model experience is valued given Arango's platform.\nHands-on experience with modern LLMs and tooling (OpenAI/Anthropic/Llama, Hugging Face, LangChain/LlamaIndex, function and tool calling).\nRetrieval and vector databases (FAISS, pgvector, Pinecone, Weaviate, or similar), and hybrid retrieval that combines graph and vector.\nCloud and containers (AWS/GCP/Azure), Docker/Kubernetes, IaC (Terraform/CloudFormation), and CI/CD.\nObservability (metrics, logs, traces) and performance tuning for latency-sensitive services.\nExcellent customer-facing communication, with the ability to lead technical conversations from the executive level down to the engineering team.\nLocation: Remote\n\nNice to have\nDirect ArangoDB experience, or prior work deploying a graph database in production.\nSearch and IR fundamentals (BM25, hybrid retrieval, re-ranking, ColBERT, cross-encoders).\nFront-end or full-stack experience (TypeScript/React, Next.js) for light UI prototyping.\nMLOps platforms and evaluation frameworks (MLflow, Weights & Biases, Ragas, promptfoo, DeepEval).\nModel adaptation and inference optimization awareness (LoRA/PEFT, DPO, distillation, quantization, vLLM/TGI/TensorRT-LLM), enough to advise on tradeoffs rather than to hand-build.\nDomain experience in finance, healthcare, public sector, manufacturing, or retail.\nSecurity and compliance familiarity: data residency, KMS/HSM, private networking.\nFrench government or industry experience.\nWhat success looks like (6–12 months)\n2 to 4 customer deployments of Arango's AI suite live in production against agreed uptime, latency, and cost targets.\nMeasurable quality and business outcomes (task accuracy, deflection rate, cycle time) backed by evaluation and telemetry.\nReusable graph and GraphRAG reference architectures and connectors adopted by the broader delivery team and by customers.\nCustomer teams enabled and self-sufficient, with runbooks, documentation, and training in place, and strong satisfaction and NPS.\nA credible field feedback loop feeding Arango's product and engineering roadmap.\nOur toolset\nPlatform & Graph: ArangoDB multi-model (graph, document, key-value), AQL, graph algorithms, GraphRAG\nModels & SDKs: OpenAI, Anthropic, Meta Llama, Hugging Face\nRetrieval: graph traversal plus FAISS, pgvector, Pinecone, Weaviate; rerankers (ColBERT, cross-encoders)\nPipelines & Orchestration: LangChain, LlamaIndex, Ray, Airflow\nMLOps & Evals: MLflow, Weights & Biases, Ragas, promptfoo, Great Expectations\nServing & Infra: vLLM, TGI, FastAPI/gRPC, Docker/K8s, Terraform, GitHub Actions\nObservability & Guardrails: OpenTelemetry, Prometheus/Grafana, Llama Guard/Content Safety, custom filters\nData: Postgres/BigQuery/Snowflake; Kafka; object storage\nWhat Makes Arango Special?\nAt Arango, we believe that AI is only as powerful as the data foundation. Our mission is to help organizations build AI systems that can reason, decide and act based on unified, current, and trusted business context at scale. We are helping define a new category of infrastructure: the contextual data layer for AI.\nWorking at Arango means:\nContributing to cutting-edge AI and data infrastructure\nCollaborating with experienced engineers, marketers, and product leaders\nHelping shape how enterprises build AI-powered applications\nIf you're excited about the intersection of AI, data, and social media, we’d love to hear from you.\nY0kjBR4kLh","datePosted":"2026-08-06T15:07:07.121Z","dateModified":"2026-08-06T15:07:07.121Z","hiringOrganization":{"@type":"Organization","name":"Arango","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote","addressRegion":"OR","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"e443f4f2d8e0756f8dd67b96"},"url":"https://jobsearcher.com/jobs/e443f4f2d8e0756f8dd67b96"}}