About Arango:
At Arango we're on a mission to make working with complex data simple, powerful, and AI-ready. Based in California and in Cologne (with a global team), we’re building a cutting-edge data platform that helps organizations bring all their data together — graph, document, key/value, full-text, and vector search — in one engine.
Why does that matter? Because it means developers and data teams can build next-gen AI applications like RAG, knowledge graphs, and smart agents — without gluing together a bunch of tools that were never meant to work together.
Our platform makes it easy to work with any kind of data-structured, semi-structured, or unstructured, and it gives teams everything they need to build faster, smarter, and with way more context. From our easy-to-learn AQL query language to modern integration tools, we’re here to help teams grow and scale with AI.
About the role:
Arango is looking for an AI Forward Deploy Engineer to embed with our customers and deliver real, production‑grade AI solutions—fast. You’ll run discovery, design and prototype systems, ship secure and reliable services, and ensure adoption and measurable business impact. Think full‑stack AI + MLOps + product sense, delivered side‑by‑side with users.
This is a hands-on, customer-facing role for engineers who are as comfortable whiteboarding with executives as they are profiling latency in a retrieval pipeline.
Location: France - only applicants living in France will be considered (Must be fluent in French and English)
Key Responsibilities:- Partner with customer sponsors, SMEs, and operators to identify high‑value AI use cases.
- Define success metrics, SLAs/SLOs, data access needs, and a delivery plan (PoV → pilot → production)
- Build end‑to‑end prototypes (data connectors, RAG pipelines, prompts/tools, UIs/APIs).
- Productionize into secure, observable services with CI/CD, infrastructure‑as‑code, and proper testing
- Implement retrieval‑augmented generation (chunking, embeddings, ranking, caching) and tool/call orchestration.
- Evaluate and iterate prompts, models, and retrieval strategies using offline/online metrics and A/B tests.
- Where needed, fine‑tune or adapt models (LoRA/PEFT, preference optimization/DPO, distillation) and optimize inference (quantization, batching, vLLM/TGI/TensorRT‑LLM)
- Build robust data pipelines (ETL/ELT), vector indices, and metadata governance.
- Monitor quality, drift, hallucination/guardrail events, latency, and cost; set up alerting and dashboards
- Implement role‑based access, secrets management, audit logging, PII redaction, and content safety filters.
- Align solutions to customer requirements (e.g., SOC2/ISO 27001, GDPR/CCPA, HIPAA as applicable)
- Document architectures and playbooks; train customer engineers and end users.
- Capture product feedback and influence [Company]’s roadmap with field learnings.
- 4+ years of software engineering experience building and operating production systems (or equivalent).
- Strong AI, Python skills; solid understanding of data structures, networking, concurrency, and systems design.
- Hands‑on experience with modern LLMs and tooling (e.g., OpenAI/Anthropic/Llama, Hugging Face, LangChain/LlamaIndex, function/tool calling).
- Retrieval and vector databases (FAISS, pgvector, Pinecone, Weaviate, or similar).
- Cloud & containers (AWS/GCP/Azure), Docker/Kubernetes, IaC (Terraform/CloudFormation), and CI/CD.
- Observability (metrics, logs, traces) and performance tuning for latency‑sensitive services.
- Excellent communication; experience working directly with customers or cross‑functional stakeholders.
Nice to have:
- Front‑end or full‑stack experience (TypeScript/React, Next.js) for light UI prototyping.
- Search/IR fundamentals (BM25, hybrid retrieval, re‑ranking).
- MLOps platforms (MLflow, Weights & Biases), evaluation frameworks (Ragas, promptfoo, DeepEval).
- Inference optimization (vLLM, Text Generation Inference, Triton, TensorRT‑LLM, quantization).
- Domain experience in [finance/healthcare/public sector/manufacturing/retail].
- Security/compliance familiarity; prior work with data residency, KMS/HSM, or private networking.
- Government/industry clearances where relevant.
- 2–4 customer use cases deployed to production with agreed‑upon uptime, latency, and cost targets.
- Demonstrable quality lifts (e.g., task accuracy, deflection rate, cycle time) backed by evals and telemetry.
- Reusable building blocks (templates/operators/connectors) adopted by the broader delivery team.
- Customer enablement completed (runbooks, docs, training) with strong satisfaction/NPS.
- Models & SDKs: OpenAI, Anthropic, Meta Llama, Hugging Face
- Retrieval: 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
- Secure RAG assistant over millions of documents with hybrid search, guardrails, and cost‑aware caching.
- High‑volume customer support copilot that integrates with CRM/ITSM and meets a strict response‑time SLO.
- Document intake pipeline with PII detection/redaction and structured extraction (LLM + regex + heuristics).
- Fine‑tuned classification/routing model that reduces human triage by 40%+.
Why Join Arango:
Our headquarters is in San Francisco (US) and we have an office in Cologne (Germany), but most of our diverse team works remotely worldwide. So, do you prefer your desk at home or do you want to join us at one of our locations? Your choice.
The global minds of Arango team comes from 5 different continents and more than 20 countries. Diverse backgrounds enable us to see new solutions. We invite people from every culture, national origin, religion, sexual orientation, gender identity or expression, and of every age to apply to our positions. All employment decisions are based on business needs, job requirements, and individual qualifications. Arango is committed to a workplace free of discrimination and harassment based on any of these characteristics. We love this diversity and encourage everyone curious and visionary to join the multi-model movement.
Top Skills
What We Do
ArangoDB is the most scalable open-source graph database, with more than 12,000 stargazers on GitHub. Building on the concept of ‘graph and beyond’, ArangoDB combines the analytical power of graphs with JSON documents, a key-value store, and a full-text search engine, enabling developers to access and combine all of these data models with a single, elegant, declarative query language. It serves as the scalable backbone for graph analytics and complex data architectures across many industries. Founded in 2015, ArangoDB Inc. is a privately-held company backed by Bow Capital, Iris Capital, New Forge, and Target Partners. It is headquartered in San Francisco and Cologne, Germany, with offices and employees around the world. Learn more at www.arangodb.com.
