This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Customer Solution Architect — Arango AI Product Suite based in Switzerland.
This is a senior customer-facing technical role at the intersection of solution architecture, graph data, and applied AI.
You will own technical engagements from initial discovery through production deployment and expansion.
The role focuses on turning complex customer challenges into scalable architectures using multimodel data and GraphRAG capabilities.
You will work directly with executive stakeholders and engineering teams to define solutions, prove value, and guide adoption.
Deep expertise in graph technologies, retrieval systems, and modern AI tooling will be central to your success.
You will also shape reusable architectures, influence product direction, and enable customers to become self-sufficient.
This is an opportunity to help enterprises move AI applications from experimentation into reliable, production-grade systems.
Accountabilities
- Own the technical customer relationship throughout the full lifecycle, from discovery and solution design through pilots, production deployment, and expansion.
- Lead discovery sessions with executives, domain experts, and technical teams to identify high-value AI use cases and connect proposed solutions to measurable business outcomes.
- Design target architectures using multimodel data platforms, including graph schemas, query and traversal patterns, knowledge graphs, and GraphRAG retrieval strategies.
- Define success criteria, SLAs/SLOs, governance requirements, data access controls, and phased delivery plans that support a smooth path from proof of value to production.
- Build reference implementations and prototypes covering graph models, data connectors, GraphRAG pipelines, APIs, tool calling, and AI agent orchestration.
- Guide customers through secure and observable production deployments, including CI/CD, infrastructure-as-code, testing, monitoring, and operational readiness.
- Architect hybrid retrieval solutions combining graph traversal, vector search, embeddings, chunking, ranking, caching, and reranking techniques.
- Establish evaluation frameworks for AI solutions and continuously improve prompts, models, retrieval strategies, and graph structures using relevant metrics and testing approaches.
- Design data pipelines, graph ingestion processes, vector indexes, and metadata governance frameworks to support reliable AI applications.
- Implement monitoring and alerting for quality, model drift, hallucinations, guardrail events, latency, performance, and cost.
- Advise customers on security and compliance requirements, including access controls, secrets management, audit logging, PII protection, and applicable regulatory standards.
- Produce architecture documentation, operational runbooks, reusable implementation patterns, and training materials while enabling customer teams to operate solutions independently.
- Serve as a voice of the customer internally, sharing field insights and technical requirements with product and engineering teams to influence future solutions.
Requirements
- 5+ years of professional experience in software engineering, solution architecture, or technical professional services, including experience building and operating production systems.
- Deep hands-on expertise in graph data modeling, graph queries and traversal, graph algorithms, knowledge graphs, and graph-based AI applications, with direct experience building GraphRAG or knowledge-graph-backed retrieval systems for LLM applications.
- Strong Python and applied AI skills, along with a solid understanding of data structures, systems design, concurrency, and networking.
- Experience working with graph, NoSQL, key-value, and document databases, with multi-model database experience considered valuable.
- Hands-on experience with modern LLMs and AI frameworks such as OpenAI, Anthropic, Llama, Hugging Face, LangChain, LlamaIndex, function calling, and tool calling.
- Strong knowledge of retrieval and vector technologies such as FAISS, pgvector, Pinecone, Weaviate, or comparable solutions, including hybrid graph-and-vector retrieval.
- Experience with cloud and container technologies such as AWS, GCP, or Azure, along with Docker, Kubernetes, Terraform or CloudFormation, and CI/CD practices.
- Familiarity with observability practices, including metrics, logs, traces, monitoring, and performance optimization for latency-sensitive systems.
- Strong understanding of search and information retrieval concepts such as BM25, hybrid retrieval, reranking, ColBERT, or cross-encoders is advantageous.
- Experience with front-end or full-stack technologies such as TypeScript, React, or Next.js for lightweight prototyping is a plus.
- Familiarity with MLOps and evaluation tools such as MLflow, Weights & Biases, Ragas, promptfoo, or DeepEval is beneficial.
- Excellent customer-facing communication skills, with the ability to lead technical discussions with senior executives as well as hands-on engineering teams.
- Strong problem-solving, collaboration, and consulting skills, with the ability to translate complex technical concepts into practical business outcomes.
- Experience in finance, healthcare, public sector, manufacturing, or retail is advantageous, as is familiarity with security, compliance, data residency, and private networking requirements.
Benefits
- Opportunity to work on cutting-edge AI, graph, and contextual data infrastructure.
- Senior-level exposure to enterprise customers and complex AI transformation initiatives.
- Opportunity to influence product and engineering roadmaps through direct customer insights.
- Exposure to modern AI, retrieval, MLOps, cloud, infrastructure, and observability technologies.
- Remote work environment with collaboration across experienced engineering, product, and business teams.
- Opportunity to develop reusable architectures and solutions that can influence broader customer deployments.
- Meaningful role in helping organizations move AI applications from experimentation to reliable production systems.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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