VP - R&D - AI Core Services DevelopmentLocation

Rand; Greater London; Hybrid; GBP 100,000 - 125,000; Full time

Job summary

A technology consulting firm is seeking an AI Graph Engineer (Senior) to work on designing agentic workflows that enable natural-language querying across data systems. The role requires deep knowledge in LangGraph and significant experience in the Oil & Gas industry. Candidates should possess strong Python skills and expertise in knowledge graphs. This position offers hybrid working with opportunities for a permanent role upon successful completion. High salary rates are on offer.

Qualifications

• 6–10 years of experience in data engineering, AI engineering, or knowledge graph engineering.
• Hands-on experience with LangChain/LangGraph or agentic AI frameworks.
• Experience in Oil & Gas digital transformation projects.

Responsibilities

• Define and build semantic models, ontologies, and taxonomies aligned with Oil & Gas industry.
• Design and build LangGraph‑based workflows for data exploration and analytics.
• Connect AI agents with various databases and build data pipelines.

Skills

LangGraph

Python (advanced proficiency)

RDF/OWL

graph databases (Neo4j, Neptune)

natural language query systems

AI/ML skills

Tools

Docker

Kubernetes

PostgreSQL

Job description

Job Title

AI Graph Engineer (Senior)

Positions Available

2

Salary

High rates on offer, contact for details

Location

Abingdon, outside London

Hours

Full time Monday to Friday

Hybrid Working

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, treated as a probationary period; successful completion may lead to a permanent staff position or contract extension.

Key Experience Required

• 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. The role focuses 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.

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 of building ontologies, semantic models, RDF/OWL, SPARQL; graph databases (Neo4j, Neptune or similar); relational databases (PostgreSQL, SQL Server, MySQL, Oracle) and 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 (LLM‑based systems, prompt engineering, structured agent design).
• 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.
• 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.

Preferred Qualifications

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