Customer Solution Architect — Arango AI Product Suite

• 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.

• 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.

• 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.

• 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.

• 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?

• Contributing to cutting-edge AI and data infrastructure
• Collaborating with experienced engineers, marketers, and product leaders
• Helping shape how enterprises build AI-powered applications