Data Architect

About the role

We are seeking a senior Data Architect to lead the design and implementation of advanced data and AI solutions across the enterprise. The role focuses on building AI‑ready data platforms, integrating large language models, and delivering autonomous AI agents that drive operational efficiency.

Key responsibilities

• Evaluate, test and select technologies for data and AI architecture, covering proof‑of‑concept, scale‑up and production deployment.
• Architect enterprise data platforms optimized for AI, ML and LLM inference at scale.
• Define AI‑ready data patterns, including fine‑tuning, retrieval‑augmented generation and prompt engineering workflows.
• Design hybrid on‑premise and cloud architectures that integrate LLM services with core data platforms.
• Create reference patterns for multi‑model AI environments operating on shared, governed data.
• Develop semantic layers, indexing and similarity‑search mechanisms for low‑latency AI workloads.
• Build knowledge‑graph architectures to support reasoning and contextual understanding.
• Establish mechanisms for data refresh, accuracy, relevance and observability of retrieval performance.
• Design multi‑agent architectures, guardrails, auditability and feedback loops for autonomous agents.
• Assess AI models, platforms and tools against defined benchmarks and provide guidance on vendor artefacts.

Required profile

• Extensive experience in data and AI architecture/engineering, with a strong background in cloud platforms and integration patterns.
• Deep domain knowledge of the Oil & Gas industry.
• Proven ability to enhance enterprise analytics by integrating AI/ML models and large‑scale data processing frameworks.
• Strong analytical mindset focused on data‑driven decision making and operational improvement.

Required skills

• Data Architecture
• Artificial Intelligence (AI) and Machine Learning (ML)
• Large Language Model (LLM) integration
• Cloud Platforms (e.g., Azure, AWS, GCP)
• Data Integration and Ingestion patterns
• Knowledge Graph design
• Semantic Layer and Indexing
• Similarity Search and low‑latency AI workloads
• Multi‑Agent system design
• Prompt Engineering, Retrieval‑Augmented Generation, Fine‑tuning
• Observability, benchmarking and vendor evaluation