Job description

Customer Solution Architect — Arango AI Product Suite About Arango: Arango delivers a unified, natively multimodel contextual data platform that powers AI agents, assistants, and applications with the unified, current, and trusted business context needed to reason, decide, and act at scale. The Arango Contextual Data Platform connects fragmented enterprise data with LLMs, copilots, and AI agents through a simplified architecture delivered out of the box. By combining graph, vector, document, key-value, and search capabilities in a single platform, Arango eliminates the complex stacks many organizations build to operationalize enterprise AI. Trusted by organizations including NVIDIA, HPE, the London Stock Exchange, PSI CRO, the U.S. Air Force, NIH, Siemens, Transient.AI , Matpriskollen, 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. Learn more at arango.ai , LinkedIn , and G2 . 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. • 3+ 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. 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 Location: San Francisco, CA (Remote) 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.

Requirements

Must-haves

Customer Solution Architect — Arango AI Product Suite
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.

Nice-to-haves

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.

Application prep

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aiawsci/cdllmpythongcpazuredockerkubernetestypescriptreactnext.jspostgres

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