Emil Pastor
Emil Pastor
About
Detail
Head of Solutions Engineering, ANZ
Sydney, New South Wales, Australia
I’m an AI and Data technology leader focused on turning advanced capabilities into production systems that deliver measurable business outcomes.
Over the past decade, I’ve worked across Teradata, EY, McKinsey (QuantumBlack), Microsoft, and now Neo4j, helping enterprises design and operationalise AI and data platforms at scale. My work sits at the intersection of architecture, strategy, and execution. Not just what’s possible, but what actually works in complex, real-world environments.
I currently lead Solutions Engineering for ANZ at Neo4j, operating as a player-coach. I partner with banks, telcos, government and other enterprise organisations to bring AI and data systems into production, from intelligent decisioning in lending to real-time fraud detection and enterprise-scale platforms.
My focus is on building AI and data systems that can be trusted.
Explainability, governance, and traceability are not afterthoughts. They are core design principles.
A few principles I stand by:
• Thinking and problem framing are the differentiators, not execution speed
• Explainability is a systems design problem, not a reporting layer
• AI is only as good as the data and architecture behind it
• Reasoning should be captured, structured, and auditable
• Production AI succeeds or fails on trust, not model accuracy alone
I’m particularly interested in how AI and data platforms come together to support high-stakes, auditable decision-making.
If you’re working on scaling AI and data beyond demos into production, or thinking about making systems more reliable, explainable, and defensible, I’m always open to a conversation.
Over the past decade, I’ve worked across Teradata, EY, McKinsey (QuantumBlack), Microsoft, and now Neo4j, helping enterprises design and operationalise AI and data platforms at scale. My work sits at the intersection of architecture, strategy, and execution. Not just what’s possible, but what actually works in complex, real-world environments.
I currently lead Solutions Engineering for ANZ at Neo4j, operating as a player-coach. I partner with banks, telcos, government and other enterprise organisations to bring AI and data systems into production, from intelligent decisioning in lending to real-time fraud detection and enterprise-scale platforms.
My focus is on building AI and data systems that can be trusted.
Explainability, governance, and traceability are not afterthoughts. They are core design principles.
A few principles I stand by:
• Thinking and problem framing are the differentiators, not execution speed
• Explainability is a systems design problem, not a reporting layer
• AI is only as good as the data and architecture behind it
• Reasoning should be captured, structured, and auditable
• Production AI succeeds or fails on trust, not model accuracy alone
I’m particularly interested in how AI and data platforms come together to support high-stakes, auditable decision-making.
If you’re working on scaling AI and data beyond demos into production, or thinking about making systems more reliable, explainable, and defensible, I’m always open to a conversation.
