AI Graph Engineer in Milton

Positions Available: x2

Salary: High rates on offer, contact for details - Initial daily pay rate for contract period before converting to salary

Location: Abingdon, outside London

Hours: Full time Monday to Friday

Hybrid: Hybrid working with 2-3 office days in Abingdon - fully remote may be an option for the right candidate

Contract: FULL TIME initial 6 month contract, they are treating this as a probation type period, and if all goes well in the first 3-6 months then they will transition you into a permanent staff position or extend the contract if preferred

Key Experience Required: someone that has experience defining and building semantic models, ontologies, and taxonomies aligned with Oil & Gas industry data.

About the Role

We are seeking a highly skilled AI Agent Engineer with deep experience in LangGraph, agentic AI workflows, ontology-driven knowledge graphs, and data systems integration. This role will focus on designing and building agentic workflows that enable natural-language querying across structured and unstructured data to deliver intelligent insights for analytics and decision-making. Candidate will architect and implement multi-step AI agents, integrate them with enterprise data platforms, and build semantic layers that support reasoning, retrieval, planning, and autonomous task execution across heterogeneous data sources. Experience in Oil & Gas data domains such as drilling, production, subsurface, HSE, or asset operations is highly preferred.

Key Responsibilities

• Knowledge Graph & Ontology Engineering
  • Define and build semantic models, ontologies, and taxonomies aligned with Oil & Gas industry data.
  • Architect and maintain knowledge graphs that integrate with enterprise data sources.
  • Implement embeddings-assisted retrieval, RAG pipelines, and cross-domain entity linking.
• AI Agent & Workflow Development
  • Design, build, and scale LangGraph-based agentic workflows for natural-language data exploration, insights generation, and analytics automation.
  • Implement autonomous workflows including planning, retrieval, reasoning, and tool execution.
  • Build modular, stateful agents capable of multi-step reasoning, context retention, and complex decision flows.
• Data Systems Integration
  • Connect AI agents with relational databases (PostgreSQL, SQL Server, Oracle), graph databases (Neo4j, Neptune), and data lakes (S3, ADLS, Delta Lake).
  • Build pipelines to ingest, index, and query both structured and unstructured data.
  • Develop semantic query layers for NL-to-SQL, NL-to-GraphQL, or NL-to-SPARQL translations.
• Application & API Development
  • Build Python services, APIs, and microservices for agent orchestration and data access.
  • Collaborate with data engineering, analytics, and domain experts to deploy scalable solutions.
• Oil & Gas Domain Expertise
  • Understand industry data models such as drilling logs, production data, wellbore schemas, seismic metadata, engineering documents, and operations workflows.
  • Translate industry use cases into agentic AI workflows that deliver actionable insights.

Required Skills & Experience

• Core Technical Skills
  • LangGraph for agent orchestration (planning, memory, tools, multi-agent workflows).
  • Python (advanced proficiency).
  • Knowledge Graphs: building ontologies, semantic models, RDF/OWL, SPARQL.
  • Graph Databases: Neo4j, Neptune or similar.
  • Relational Databases: PostgreSQL, SQL Server, MySQL, Oracle; query optimization.
  • Data Lakes: S3, ADLS, Delta Lake, Parquet/Arrow.
  • RAG / Vector Databases: Postgres, Pinecone, Weaviate, Qdrant, Chroma or equivalent.
  • Natural Language Query Systems: NL-to-SQL, semantic query engines, embedding models.
• AI/ML Skills
  • Experience with LLM-based systems, prompt engineering, and structured agent design.
  • Knowledge of retrieval strategies, hybrid search, and memory architectures.
  • Familiarity with OpenAI, Azure OpenAI, Anthropic, or similar model providers.
• Architecture & Engineering Skills
  • Microservices architecture, API development, containerization (Docker/Kubernetes).
  • CI/CD and production ML/AI deployment best practices.
• Industry Skills
  • Oil & Gas data models and standards (PPDM, WITSML, PRODML, RESQML preferred).
  • Understanding of drilling operations, production operations, subsurface data, or engineering documents.

Preferred Qualifications

• 6-10 years of experience in data engineering, AI engineering, or knowledge graph engineering.
• 1 year hands-on experience with LangChain/LangGraph or agentic AI frameworks.
• Experience designing enterprise-scale semantic or knowledge-centric systems.
• Prior experience implementing NLQ (natural language query) for analytics or BI.
• Experience in Oil & Gas digital transformation projects.

Soft Skills

• Excellent problem-solving and conceptual modeling skills.
• Ability to work cross-functionally with data engineering, cloud teams, and business SMEs.
• Strong communication and technical documentation skills.
• Ability to translate ambiguous business requirements into technical workflows.