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About This Role
Principal Solutions Architect (Professional Services) – AI \& Deployment
About ArangoDB
Arango makes your business data AI\-ready, giving agents, apps, and assistants trusted context at scale. Every answer is traceable. 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 hiring a Customer Solution Architect to be the primary professional services interface between Arango and the customers deploying our AI product suite. You own the technical relationship end to end, from first discovery through production and expansion. Your job is to turn a customer's problem into a working architecture on Arango's multi\-model platform and its GraphRAG and knowledge\-graph capabilities, prove value early, and guide the customer's team through deployment and adoption.
The role sits where solution architecture, graph data modeling, and applied AI meet. It suits someone who can hold a design conversation with a customer's chief architect in the morning and review a GraphRAG retrieval design with their engineers in the afternoon.
Deep graph expertise is not optional here. It is the core of how Arango's AI suite delivers value, and the CSA is expected to be the customer's most trusted source of graph and GraphRAG design judgment.
Key responsibilities
- 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.
Required qualifications* 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.
Nice to have* 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.
What success looks like (6–12 months)* 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.
Our toolset* 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
Why Join Arango:
At Arango, we believe that AI is only as powerful as the data foundation. Our mission is to help organizations build AI systems that can reason, decide and act based on unified, current, and trusted business context at scale. We are helping define a new category of infrastructure: the contextual data layer for AI.
Working at Arango means:* Contributing to cutting\-edge AI and data infrastructure
- Collaborating with experienced engineers, marketers, and product leaders
- Helping shape how enterprises build AI\-powered applications
If you're excited about the intersection of AI, data, and social media, we’d love to hear from you.
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Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ARANGO, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
ARANGO AI Hiring
ARANGO has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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