AI Forward Deploy Engineer

Location: Paris, France

Department: General

About Arango:

Arango makes your business data AI-ready, giving agents, apps, and assistants trusted context at scale. Every answertraceable. Every decision is governed. No more stitching together a vector store, a graph database, a search index, and a governance layer added as an afterthought. Arango’s Contextual Data Platform has it all built in, not bolted on.

Trusted by organizations including NVIDIA, HPE, Zscaler, the London Stock Exchange, the U.S. Air Force, NIH, Siemens, 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.

Stop building Frankenstacks. Start building with Arango. Learn more at arango.ai

We believe great innovation happens when curious, driven people collaborate. We are committed to building a diverse and inclusive team and supporting our employees and interns as they learn, grow, and contribute to shaping the future of enterprise 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.
   

Required Qualifications:

• 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.
• Strong database skills; Graph, NoSQL, Key Value
• 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.
• French Government/industry experiences
   

What Success Looks Like (6–12 months):

• 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.
   

Our Toolset:

• 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 storag