Data Architect

Role purpose and contract

Data and AI architecture

• Evaluate, test, and design end-to-end data and AI architectures for enterprise and agentic use cases from proof of concept through production deployment.
• Architect enterprise data platforms for AI, ML, and LLM integration and inference at scale.
• Define architecture patterns supporting fine-tuning, retrieval-augmented generation, prompt engineering, and hybrid on-premises/cloud LLM services.
• Define multi-model AI environments on shared governed data, semantic layers, indexing, similarity search, and enterprise knowledge graphs.
• Design mechanisms for data refresh, accuracy, relevance, and retrieval-performance and latency observability.

Agentic AI and model lifecycle

• Design multi-agent architectures for cross-functional workflows, including decision logic, tool use, and interaction patterns for autonomous agents.
• Design guardrails, auditability, controls, and feedback loops for agent actions; define prompt orchestration patterns for multi-step reasoning.
• Assess AI models, platforms, and tools using evaluation criteria and benchmarks, and review vendor AI, ML, and advanced analytics architecture artefacts.
• Design data pipelines for continuous model training, evaluation, deployment, and retraining; design ML CI/CD monitoring accuracy, latency, and cost.
• Design cost-efficient inference and fine-tuning approaches with usage tracking and controls.

Enterprise data architecture and governance

• Guide data integration and ingestion across enterprise platforms and analytics solutions in alignment with data modelling and MDM standards.
• Design batch, streaming, and real-time ingestion patterns and define medallion, Kappa, and Lambda reference architectures.
• Maintain NOC data architecture and document reference architectures, solution patterns, designs, dashboards, reports, model representations, dictionaries, and data models.
• Ensure alignment with MDM principles, data and AI governance, information security, enterprise architecture, and PMO standards.
• Support privacy-preserving model training, security controls, and access patterns for data and AI platforms.
• Support Architecture Review Board and Design Advisory reviews, provide structured feedback to delivery teams, and guide data operations and governance teams.

Education and experience

• Bachelor’s degree in Computer Science, Computer Engineering, or a relevant field; specialization and certification in AI, ML engineering, and/or Data Science.
• At least eight years of relevant experience in technical AI implementation, data architecture, AI model development, and technical robustness of AI systems.
• Experience with supervised and unsupervised learning, deep learning, neural networks, CNNs, RNNs, NLP, computer vision, or text analytics.
• Hands-on experience with data agents, operational agents, and agentic AI; track record delivering AI/data digital products in industry.
• Strong experience with AI-ready data platforms, data architecture and engineering, AI/ML architecture, and ML lifecycle delivery from proof of concept to scale-up.

Technical skills and preferred platforms

• Strong understanding of software development lifecycle and DevOps practices; experience designing training, evaluation, and retraining pipelines, including CI/CD.
• Experience with agile tools such as Jira or Confluence, or similar.
• Experience on projects using industry data platforms such as OSDU or Cognite Data Fusion is a plus.
• Role purpose calls for strong Oil & Gas domain knowledge.