Senior AI Engineer (LLMs & Knowledge Graphs)
- Role
- AI / ML
- Experience
- Senior
- Employment
- Contract
- Company size
- Enterprise
Open to Anywhere in Europe · UTC-2…UTC+2. Set where you work from to check your eligibility.
No BS summary
Our client, a Fortune 50 leader in enterprise solutions and innovations, is seeking a Senior AI Engineer with Knowledge Graphs and LLMs skills to join their AI incubator to scout, incubate, and validate internal ideas.
Core skills
Required skills
Optional skills
Our client, a Fortune 50 leader in enterprise solutions and innovations, is seeking a Senior AI Engineer with Knowledge Graphs and LLMs skills to join their AI incubator to scout, incubate, and validate internal ideas. This role is part of a high-impact strategy leveraging Graph Neural Networks (GNNs) and Generative AI to redefine workflows, semantic search, and intelligence for enterprise solutions in Finance, Operations, Supply Chain, Engineering, or Investments.
This is a long-term remote-first contract position with a required overlap of US working hours (2-6 PM CET) .
Responsibilities
Build Agentic Workflows: Implement orchestration, retrieval pipelines, and validator agents using graph-aware tools.
Optimize Retrieval: Build hybrid search pipelines (lexical + vector) and integrate vector databases like FAISS, Milvus, or Pinecone.
Model Integration: Integrate LLMs (Azure OpenAI, Anthropic) and support domain-specific fine-tuning or adapter models.
Scalable Engineering: Develop robust API endpoints and ETL pipelines to support model and agent runtimes.
Experiment & Evaluate: Create evaluation suites for reliability, drift detection, and performance optimization.
Work Conditions
Type: Full-time & Long-term contract work
Start Date : ASAP
Location : Remote (99%) in Europe; must be able to travel freely within Europe for workshops.
US Time Zone Overlap : Required ( 2 PM - 6 PM CET )
Contract with European LLC
Python Expertise: 3+ years of strong Python engineering experience.
Graph Intelligence and Databases: Working knowledge of knowledge graph modeling (schemas, ontologies, entity resolution) and graph databases. Hands-on experience with Neo4j, Memgraph, AWS Neptune, ArangoDB, or similar. Familiarity with graph embeddings and GNNs (GCN/GAT) is a plus.
Evaluation & Experimentation: Comfortable designing experiments, building eval harnesses, and reasoning about model quality, robustness, and bias in production AI systems.
Modern AI Patterns: Hands-on experience building RAG pipelines and agentic workflows. Comfort with prompt engineering and tool/function calling. Experience building text-to-SQL or semantic parsing capabilities over structured data sources.
LLM Observability: Familiarity with LLM evaluation frameworks (e.g., Ragas, DeepEval, Langfuse) and production monitoring of AI systems.
Retrieval & Search: Lexical + vector + hybrid retrieval, embeddings, and reranking. Experience incorporating user and context signals for personalization.
Fine-tuning & Adaptation: Experience with fine-tuning and adaptation patterns (e.g., LoRA/QLoRA, instruction tuning, embedding model fine-tuning).
APIs & Integrations: Solid knowledge of APIs, microservices, and data-centric integrations.
Engineering Discipline: Solid software engineering fundamentals - clean code, testing, debugging, code reviews, and comfort working in agile pods.
Cloud & Deployment: Experience with AWS/Azure/GCP and CI/CD workflows.
Excellent problem-solving skills and keen attention to detail.
Ability to participate in the discussions and lead the technical discussions
Have a consultancy mindset → always try to find a solution for the client
What you'll do
- Build Agentic Workflows: Implement orchestration, retrieval pipelines, and validator agents using graph-aware tools.
- Optimize Retrieval: Build hybrid search pipelines (lexical + vector) and integrate vector databases like FAISS, Milvus, or Pinecone.
- Model Integration: Integrate LLMs (Azure OpenAI, Anthropic) and support domain-specific fine-tuning or adapter models.
- Scalable Engineering: Develop robust API endpoints and ETL pipelines to support model and agent runtimes.
- Experiment & Evaluate: Create evaluation suites for reliability, drift detection, and performance optimization.
What they require
- 3+ years of strong Python engineering experience.
- Working knowledge of knowledge graph modeling (schemas, ontologies, entity resolution) and graph databases. Hands-on experience with Neo4j, Memgraph, AWS Neptune, ArangoDB, or similar.
- Comfortable designing experiments, building eval harnesses, and reasoning about model quality, robustness, and bias in production AI systems.
- Hands-on experience building RAG pipelines and agentic workflows. Comfort with prompt engineering and tool/function calling. Experience building text-to-SQL or semantic parsing capabilities over structured data sources.
- Familiarity with LLM evaluation frameworks (e.g., Ragas, DeepEval, Langfuse) and production monitoring of AI systems.
Our client
a Fortune 50 leader in enterprise solutions and innovations