Milos Colic — VP of Engineering @ Xoople | AI and Product Engineering Leadership | The first Earth data infrastructure company, built for AI
VP of Engineering @ Xoople | AI and Product Engineering Leadership | The first Earth data infrastructure company, built for AI
Milos Colic ranks #362 of 18,566 LinkedIn creators in Information Services, and is a standout voice in United Kingdom. They have 5.2K followers and published 57 posts in the last 60 days at a 0.5% average engagement rate.
- 5.2K followers
- 57 posts / 60d
- 0.5% avg engagement
- 446 follower growth / 30d
The roast
Milos claims to build data infrastructure for the real world, yet his content engagement rate is a rounding error away from not existing at all. He spends his days at Xoople trying to solve spatial gaps while his entire career profile remains stuck in the same place.
About Milos
I lead product engineering organizations, building trusted data and AI products with clarity, pace, and care. I am most useful when complex technical capability has to become something an organization can understand, trust, sell, and scale. That work usually sits between product, engineering, commercial reality, and judgment. The hard part is rarely the technology alone. It is deciding what matters, making the work legible, building the team that can carry it, and turning possibility into something people can depend on.At Xoople, I built the product engineering function from zero to one and now lead it as we build Earth intelligence infrastructure for AI.Before that, I led product engineering across CARTO’s core product surface, from developer platforms to end-user applications. At Databricks, I helped grow public sector and geospatial data opportunities in UK&I, worked closely with product and engineering teams, and created GeoBrix, a geospatial analytics framework adopted by hundreds of organizations. Earlier, at Barclays and Amadeus, I worked on regulated analytics, decision systems, large-scale data products, and patented NLP research.The thread through all of it is simple: technical depth only creates value when it changes decisions, systems, and outcomes. I care about teams that can think clearly under pressure, challenge theatre, build judgment, and leave behind systems stronger than any one person.I write about AI, product engineering, leadership, organizational transformation, and the practical work of turning technical possibility into real-world value.
Highlights
- Top 1% Creator — 57 posts in 30 days
- Top 5% in Information Services — Ranked #1 of 81 creators
- Top 5% in United Kingdom — Ranked #60 of 1840 creators
- Top 25% Engager — 0.53% engagement rate
Recent posts
I love Jim Collins’s Hedgehog Concept; I came across it again during my MasterClass Executive Strategy coursework. It got me thinking that the Hedgehog applies evermore strongly as AI improves. Shallow knowledge is commoditised, and so is basic retrieval work. And this applies to both companies and individuals. The hedgehog concept asks you to focus on the intersection of three things: - what you are deeply passionate about - what you can become best in the world at - what drives your economic engine. This is honestly the same concept as the popular framing of Ikigai if you apply it to ind
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CARTO’s recent work on spatial semantics for AI Agents made me think back to how much of working with geospatial data depended on things we knew, but the data itself never actually said. An H3 index is a good example. Without more context, as CARTO puts it, the agent “sees a string.” Someone working with H3 knows that it represents a spatial cell, that it has a resolution and that it can be rolled up. But none of this is obvious from the underlying storage type. CARTO is using semantic models based on Apache Ossie to add this context. In their example, the model tells the agent that the fiel
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Looking at Spark 4.2, there is one thing that gets me excited, Data Source V2 is getting much more operational responsibility. DS V2 itself has been around for years, but Spark 4.2 deepens it with transaction management, CDC connector APIs, schema evolution, richer operational metrics and enhanced partition-stat filtering. At the same time, Spark is gaining native GEOMETRY and GEOGRAPHY types (https://lnkd.in/gaZi8uZv). Historically, while building Mosaic, this was where I spent quite some time making shapefiles, File Geodatabases and other esoteric GDAL-backed formats work inside Spark as if
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Last updated 2026-08-01