Job description
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
Requirements
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
Highlights
If you are passionate about AI, Graph-centric AI, Python, and building next-generation agentic workflows, this role with our client offers an exciting opportunity to work on cutting-edge R&D projects!
Originally posted on Himalayas
Who can apply
The source lists worldwide eligibility. Accepted UTC offsets: UTC-11, UTC-10, UTC-9.5, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC-4, UTC-3.5, UTC-3, UTC-2, UTC-1, UTC+0, UTC+1, UTC+2, UTC+3, UTC+3.5, UTC+4, UTC+4.5, UTC+5, UTC+5.5, UTC+5.75, UTC+6, UTC+6.5, UTC+7, UTC+8, UTC+8.75, UTC+9, UTC+9.5, UTC+10, UTC+10.5, UTC+11, UTC+12, UTC+12.75, UTC+13, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.