客户解决方案架构师 — Arango AI 产品套件
Customer Solution Architect — Arango AI Product Suite
客户解决方案架构师 — Arango AI 产品套件
关于 ArangoDB
Arango 让您的业务数据具备 AI 就绪能力，为代理、应用和助手提供可扩展的可信上下文。每个答案都可追溯，每个决策都有管理。不再需要拼凑向量存储、图数据库、搜索索引和作为事后添加的治理层。Arango 的上下文数据平台将所有功能内置其中，而非后期添加。NVIDIA、HPE、Zscaler、伦敦证券交易所、美国空军、NIH、西门子和 Articul8 等组织都信赖 Arango，帮助企业在 AI 试点项目与可靠生产系统之间快速过渡，同时降低基础设施复杂性和总体拥有成本。Arango 是 NVIDIA Inception 计划和 AWS ISV Accelerate 计划的成员。停止构建“杂牌堆栈”，从 Arango 开始构建。了解更多请访问 arango.ai。我们相信，当好奇且有动力的人们协作时，伟大的创新就会发生。我们致力于打造一个多元化和包容性的团队，支持我们的员工和实习生在学习、成长和塑造企业 AI 未来的过程中贡献力量。
关于该职位
Arango 正在招聘一名客户解决方案架构师，作为 Arango 与部署我们 AI 产品套件的客户之间的主要专业服务接口。您将全程负责技术关系，从最初的发现到生产环境和扩展阶段。您的工作是将客户的问题转化为 Arango 多模型平台及其 GraphRAG 和知识图谱功能的可行架构，尽早证明价值，并引导客户的团队完成部署和采用。该职位位于解决方案架构、图数据建模和应用 AI 的交汇点。适合能够早上与客户的首席架构师进行设计讨论，下午与他们的工程师审查 GraphRAG 检索设计的人选。深厚的图数据专业知识在这里不是可选项，而是核心。Arango 的 AI 套件通过图数据实现价值，而客户解决方案架构师（CSA）被期望成为客户最信任的图数据和 GraphRAG 设计判断来源。
关键职责
· 作为主要专业服务联系人，全程负责技术客户关系：从发现、设计、试点、生产到扩展。
- 与客户发起人、领域专家和运营人员进行发现，识别 Arango AI 产品套件的高价值用例，并根据实际业务成果进行评估。
- 设计并实施基于 Arango 平台的解决方案，确保满足客户需求并实现业务目标。
查看英文原文
Customer Solution Architect — Arango AI Product Suite
About ArangoDB
Arango 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.
About the role
Arango 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.
Key responsibilities
· Own the technical customer relationship as the primary professional services contact across the full lifecycle: discovery, design, pilot, production, and expansion.
- Run 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.
- Design 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.
- Define success criteria, SLAs/SLOs, data access and governance requirements, and a phased delivery plan from proof of value to production.
- Build reference implementations and prototypes that prove value early: graph schema, data connectors, GraphRAG pipelines, tool and agent orchestration, APIs.
- Guide production deployment into secure, observable services alongside the customer's engineers, with CI/CD, infrastructure-as-code, and proper testing.
- Architect retrieval across graph traversal, vector search, and hybrid approaches (chunking, embeddings, ranking, caching), and orchestrate tool and agent calls.
- Establish evaluation practices and iterate on prompts, models, retrieval strategy, and graph structure using offline and online metrics and A/B tests.
- Design data pipelines (ETL/ELT), vector indices, graph ingestion, and metadata governance.
- Define monitoring for quality, drift, hallucination and guardrail events, latency, and cost, and stand up alerting and dashboards with the customer.
- Architect role-based access, secrets management, audit logging, PII redaction, and content safety controls.
- Meet customer compliance requirements (SOC 2/ISO 27001, GDPR/CCPA, HIPAA as applicable).
- Produce architecture documentation, runbooks, and reusable patterns, and train customer engineers and end users.
- Act as the voice of the customer to Arango's product and engineering teams, shaping the roadmap with what we learn in the field.
Required qualifications
- Deep 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.
- 5+ years in software engineering, solution architecture, or technical professional services, including building and operating production systems.
- Strong applied AI and Python skills, with a solid grasp of data structures, systems design, concurrency, and networking.
- Strong database skills across graph, NoSQL, key-value, and document models. Multi-model experience is valued given Arango's platform.
- Hands-on experience with modern LLMs and tooling (OpenAI/Anthropic/Llama, Hugging Face, LangChain/LlamaIndex, function and tool calling).
- Retrieval and vector databases (FAISS, pgvector, Pinecone, Weaviate, or similar), and hybrid retrieval that combines graph and vector.
- Cloud and containers (AWS/GCP/Azure), Docker/Kubernetes, IaC (Terraform/CloudFormation), and CI/CD.
- Observability (metrics, logs, traces) and performance tuning for latency-sensitive services.
- Excellent customer-facing communication, with the ability to lead technical conversations from the executive level down to the engineering team.
Location: Remote
Nice to have
- Direct ArangoDB experience, or prior work deploying a graph database in production.
- Search and IR fundamentals (BM25, hybrid retrieval, re-ranking, ColBERT, cross-encoders).
- Front-end or full-stack experience (TypeScript/React, Next.js) for light UI prototyping.
- MLOps platforms and evaluation frameworks (MLflow, Weights & Biases, Ragas, promptfoo, DeepEval).
- Model adaptation and inference optimization awareness (LoRA/PEFT, DPO, distillation, quantization, vLLM/TGI/TensorRT-LLM), enough to advise on tradeoffs rather than to hand-build.
- Domain experience in finance, healthcare, public sector, manufacturing, or retail.
- Security and compliance familiarity: data residency, KMS/HSM, private networking.
- French government or industry experience.
What success looks like (6–12 months)
- 2 to 4 customer deployments of Arango's AI suite live in production against agreed uptime, latency, and cost targets.
- Measurable quality and business outcomes (task accuracy, deflection rate, cycle time) backed by evaluation and telemetry.
- Reusable graph and GraphRAG reference architectures and connectors adopted by the broader delivery team and by customers.
- Customer teams enabled and self-sufficient, with runbooks, documentation, and training in place, and strong satisfaction and NPS.
- A credible field feedback loop feeding Arango's product and engineering roadmap.
Our toolset
- Platform & Graph: ArangoDB multi-model (graph, document, key-value), AQL, graph algorithms, GraphRAG
- Models & SDKs: OpenAI, Anthropic, Meta Llama, Hugging Face
- Retrieval: graph traversal plus FAISS, pgvector, Pinecone, Weaviate; rerankers (ColBERT, cross-encoders)
- Pipelines & Orchestration: LangChain, LlamaIndex, Ray, Airflow
- MLOps & Evals: MLflow, Weights & Biases, Ragas, promptfoo, Great Expectations
- Serving & Infra: vLLM, TGI, FastAPI/gRPC, Docker/K8s, Terraform, GitHub Actions
- Observability & Guardrails: OpenTelemetry, Prometheus/Grafana, Llama Guard/Content Safety, custom filters
- Data: Postgres/BigQuery/Snowflake; Kafka; object storage
What Makes Arango Special?
At 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.
Working at Arango means:
- Contributing to cutting-edge AI and data infrastructure
- Collaborating with experienced engineers, marketers, and product leaders
- Helping shape how enterprises build AI-powered applications
If you're excited about the intersection of AI, data, and social media, we’d love to hear from you.
Originally posted on Himalayas