2 months ago
Paris, FranceSenior
Responsibilities
- Own the technical customer relationship as the primary professional services contact from discovery through design, pilot, production, and expansion.
- Run customer discovery, identify high-value use cases, and define business outcomes, success criteria, SLAs/SLOs, governance requirements, and phased delivery plans.
- Design Arango multi-model architectures, graph data models, AQL queries and traversals, GraphRAG retrieval systems, vector and hybrid retrieval, and agent/tool orchestration.
- Build reference implementations and prototypes including graph schemas, data connectors, GraphRAG pipelines, APIs, and agent workflows.
- Guide secure, observable production deployments with testing, CI/CD, infrastructure-as-code, monitoring, alerting, and dashboards.
- Design evaluation practices and optimize prompts, models, retrieval strategies, graph structures, latency, quality, drift, hallucination, guardrails, and cost.
- Design ETL/ELT pipelines, vector indices, graph ingestion, metadata governance, role-based access, secrets management, audit logging, PII redaction, and content safety controls.
- Produce architecture documentation and runbooks, train customer engineers and end users, and feed customer insights to product and engineering teams.
Requirements
- Deep expertise in graph data modeling, graph queries and traversal, graph algorithms, and knowledge-graph design for AI, including AQL or equivalent technologies such as Cypher or Gremlin.
- Direct experience building GraphRAG or knowledge-graph-backed retrieval systems for LLM applications.
- At least 5 years of experience in software engineering, solution architecture, or technical professional services, including building and operating production systems.
- Strong applied AI and Python skills, with knowledge of data structures, systems design, concurrency, and networking.
- Strong database experience across graph, NoSQL, key-value, and document models; multi-model experience is valued.
- Hands-on experience with modern LLMs and tooling such as OpenAI, Anthropic, Llama, Hugging Face, LangChain, and LlamaIndex.
- Experience with retrieval and vector databases such as FAISS, pgvector, Pinecone, or Weaviate, including hybrid graph and vector retrieval.
- Experience with cloud and container technologies, infrastructure-as-code, CI/CD, observability, and performance tuning.
- Excellent customer-facing communication skills across executive, architect, and engineering audiences.
- Preferred qualifications include ArangoDB or production graph-database experience, search and information-retrieval fundamentals, front-end or full-stack prototyping, MLOps and evaluation platforms, model adaptation and inference optimization, relevant industry experience, and security, compliance, data residency, KMS/HSM, or private networking familiarity.
Benefits
- The position is based in Paris, France.
- The role offers the opportunity to work on AI and data infrastructure, collaborate with experienced engineering, marketing, and product teams, and help shape enterprise AI applications.
Tech Stack
Apache AirflowApache KafkaAWSAzureDockerFastAPIGitHub ActionsGoogle BigQueryGoogle Cloud PlatformGrafanagRPCKubernetesMLflowNext.jsPostgreSQLPrometheusPythonReactSnowflakeTerraformTypeScript
Categories
Solutions Engineering