Staff AI Engineer (Knowledge Graphs & Agentic Systems)

Location: Mexico (Hybrid)

? About Variacode

At Variacode, we connect top-tier tech talent with cutting-edge general projects. We are currently looking for a visionary, hands-on Staff AI Engineer (P5) to join a high-performing engineering organization pioneering next-generation autonomous systems, deep contextual retrieval, and enterprise-grade agent orchestration.

Our client's technology powers mission-critical workflows globally for Fortune 500 enterprises, orchestrating multi-step decision intelligence over massive datasets in real time. If you want to define technical strategy, lead complex architectural roadmaps, and build resilient, production-ready AI systems at massive scale, this is your place!

? The Role

Move past toy demos and single-prompt pipelines. As a Staff AI Engineer (P5), you will act as a primary technical authority driving the design, architecture, and production readiness of agentic workflows and knowledge-grounded reasoning engines.

You will bridge unstructured generative power with deterministic, structured truth by engineering resilient Knowledge Graphs (GraphRAG), autonomous multi-agent systems, and robust execution runtimes capable of handling non-deterministic failures, complex state management, and real-time enterprise demands.

⚙️ What you will do (Responsibilities):

- Architect Enterprise GraphRAG Platforms: Design and implement state-of-the-art hybrid retrieval architectures, grounding generative models on complex graph structures (Neo4j, Ontologies, Knowledge Graphs) combined with dense/sparse vector search.
- Lead Multi-Agent System Design: Build and deploy deterministic, production-grade agentic architectures using frameworks like LangGraph, AutoGen, and CrewAI, tackling state persistence, tool calling, cycle management, and dynamic routing.
- Define Technical Standards (P5 Impact): Own end-to-end technical strategy, establish architectural patterns for latency, accuracy, and fault tolerance, and mentor senior and lead engineers across AI/ML workstreams.
- Tackle Hallucinations & Alignment:



Implement rigorous evaluation frameworks (e.g., Ragas, custom eval harnesses) for grounding, faithfulness, and semantic fidelity across multi-turn reasoning workflows.
- Bridge Research & Production Engineering: Write clean, modular, production-ready Python services with robust CI/CD, caching layers, rate limiting, and observability for agent runs.
- Drive Cross-Functional Collaboration: Partner directly with Product Managers, Data Architects, and Executive Stakeholders to translate vague business challenges into deterministic, scalable AI system architectures.

? What we are looking for:

Must-haves (Required):

- Experience & Seniority: 8 years in software/data engineering with 3 years architecting applied AI/LLM applications at enterprise or high-scale levels (Staff/Principal equivalent scope).
- Core Technical Stack: Advanced Python engineering, Knowledge Graphs (Neo4j, Cypher, GraphRAG methodologies), and production multi-agent orchestration frameworks (specifically LangGraph, CrewAI, AutoGen).
- System Design & RAG Architecture: Proven track record deploying complex, hybrid RAG systems incorporating graph ontologies, vector embeddings, re-ranking models, and dynamic context windows.
- Software Rigor: Strong computer science fundamentals, deep understanding of distributed systems, concurrency/async I/O in Python, and API microservice design.
- Language Proficiency: C1 Fluent English with excellent verbal and written technical communication skills, able to present complex trade-offs to senior leadership.

Nice-to-haves (Pluses):

- Deep experience with hybrid graph-vector indices, semantic web standards (RDF, OWL, SPARQL), or property graph modeling.




- Hands-on experience with production tracing and observability platforms for agents (LangSmith, Arize Phoenix, OpenInference, TruLens).
- Familiarity with container orchestration (Docker, Kubernetes) and cloud deployments on AWS/GCP (Bedrock, SageMaker, Vertex AI).
- Track record of fine-tuning models (PEFT/LoRA) for specialized tool use, Cypher generation, or structured output extraction.

? What to Expect (Our Hiring Process):

We value your time and believe in a transparent, fast-moving recruitment journey. Here is what our process looks like:

1. Application & AI Screening: Submit your application. Our smart screening tools match your profile against core job requirements.
2. Recruiter Video Call (30 mins): A 1-on-1 video call with a Talent Acquisition Specialist to discuss your background, career goals, and cultural alignment.
3. Variacode Internal Technical Assessment & System Design Interview: A practical evaluation and architectural deep dive led by Variacode’s Technical Lead team. Given the Staff (P5) scope, this session focuses on distributed AI System Design—evaluating your ability to architect scalable GraphRAG pipelines, stateful multi-agent systems (using frameworks like LangGraph, CrewAI, or AutoGen), and robust fallback/evaluation frameworks under production constraints.
4. ? Pro-tip for candidates: Be prepared to draw and defend architectural diagrams live during the interview (e.g., entity graphs, agent loops, state persistence, context retrieval layers). We recommend practicing beforehand with tools like draw.io, Excalidraw, or Lucidchart.
5. Client / Partner Interview: Meet directly with the client's engineering or leadership team to align on technical expectations, real-world project challenges, and team synergy.
6. ? Note: Depending on the client's internal process, they may request an additional client-specific technical assessment or live site review during this stage.
7. Offer & Onboarding: Receive your formal offer, sign your agreement, and kick off your onboarding journey with Variacode!

📌 Staff AI Engineer (Knowledge Graphs & Agentic Systems) (México)
🏢 Variacode
📍 México