AI Salaries in Colombia in 2026: What to Expect by Role and Experience

In This Guide

• Introduction: Reading AI offers in Colombia’s 2026 market
• AI market snapshot and Colombia’s position in LatAm
• Why Bogotá and Medellín matter for AI careers
• Salary benchmarks by role and seniority
• How company tier reshapes your compensation
• Mapping Colombian titles to international levels
• Anatomy of a Colombian AI compensation package
• Real offer examples and net take-home estimates
• Salary percentiles: where you sit in the market
• Negotiation tactics tailored to Colombian employers
• When equity is worth the trade-off
• Bogotá versus Medellín: salary, cost and strategy
• Upskilling and Nucamp pathways to higher pay
• Checklists and an action plan to move up a band
• Frequently Asked Questions

AI market snapshot and Colombia’s position in LatAm

Seen from Bogotá or Medellín, it can feel like every other week a new AI role appears on LinkedIn. Under the surface, Colombia has become one of the region’s heaviest hitters: recent analyses of digital hiring show the country concentrating roughly 20% of Latin America’s digital talent demand, with AI-related roles among the fastest-growing segments, according to reports like Colombia’s digital jobs breakdown on LinkedIn.

This demand lands on a very short supply of true specialists. Companies in Bogotá’s financial district and Medellín’s Ruta N increasingly chase profiles that mix solid ML with MLOps, cloud, and production experience in LLMs. International rate cards now highlight Colombia as a hot spot where senior AI and MLOps salaries have grown by around 15-20% year-over-year, particularly for engineers who can ship and maintain models in production, as documented in regional benchmarking like Second Talent’s AI engineer by region report.

Regionally, Colombia sits in the middle of the Latin American pack for base salaries, but looks far stronger once you factor in cost of living and remote opportunities. Benchmarks comparing major hubs show a clear pattern:

• São Paulo paying roughly 20% more than Bogotá for senior AI roles
• Mexico City at about 15% above Bogotá
• Santiago around 10% higher, but with steeper living costs
• Bogotá ahead of Buenos Aires on local-currency contracts due to Argentina’s volatility

For Colombian engineers, that positioning creates a sweet spot: local employers in Bogotá and Medellín remain cost-conscious, but nearshore and fully remote US/EU contracts are increasingly priced off regional - not just Colombian - benchmarks. That’s why the same engineer can be “expensive” for a national bank and still “a bargain” for a Bay Area startup paying in dollars.

Why Bogotá and Medellín matter for AI careers

Walk through Bogotá’s financial district on a weekday and you’ll see why the city has become Colombia’s AI nerve center. Behind the glass facades around Calle 72 and Calle 100 sit product and data teams for Rappi, Mercado Libre, Globant, IBM, Accenture, Bancolombia and Grupo Aval, alongside engineering hubs for global players like Microsoft and Google. Feeding those teams is a dense university pipeline from institutions such as Universidad de los Andes, Universidad Nacional, Javeriana and Rosario, turning Bogotá into the main launchpad for AI and data careers in the country.

Medellín plays a different but equally powerful role. Around Ruta N and the northern innovation district, startups and nearshore firms like PSL/Perficient, Endava and Globant run AI, data and cloud projects for clients in North America and Europe. Universities such as EAFIT and UPB keep a steady flow of software and data talent, while neighborhoods like Laureles and El Poblado offer a quality of life that makes Medellín especially attractive for remote AI engineers working on US or European time zones.

Together, Bogotá and Medellín concentrate the bulk of Colombia’s AI hiring and the majority of roles that tap into international budgets. Analyses of the Colombian tech market highlight that these two metros host most nearshore and export-focused employers, which is why they’re central to guides like CloudDevs’ hiring playbook for software engineers in Colombia. Salaries for AI and data roles in both cities tend to sit noticeably above national averages, especially where English, cloud, and MLOps skills are required.

That concentration also shapes how you grow. In Bogotá and Medellín you have access to:

• Continuous meetups, hackathons and university research groups focused on ML, MLOps and LLMs
• Bootcamps like Nucamp, which runs local communities in both cities and offers AI-focused programs from about COP 8.5M-15.9M
• A wide range of employers, from conservative banks to high-growth startups and fully remote US/EU teams

Salary benchmarks by role and seniority

Once you look past shiny titles, AI offers in Colombia fall into fairly consistent bands by role and seniority. The table below shows typical gross annual base salaries (COP) for Bogotá and Medellín in tech-first companies on local contracts, built from combined market reports and benchmarks such as ERI’s machine learning engineer salary data for Colombia.

MLOps and Applied Scientist roles typically sit about 15-20% above equivalent ML Engineer levels because Colombian companies struggle to hire people who can both build and run models in production. At the very top end, senior Applied Scientists and AI Researchers in Tier 1 multinationals (for example, global cloud providers or regional leaders like Mercado Libre) can exceed COP 350M-450M total compensation once RSUs and performance bonuses are included.

By contrast, national-enterprise data from sources like SalaryExpert shows entry AI Engineers around ~79M and seniors near ~129M, and Data Scientists from roughly 75.8M to 123.1M, with a noticeable Bogotá premium on top of that, reinforcing that many corporate roles sit in the lower half of these bands (AI engineer salary analysis for Bogotá). Reading the table against your own skills and employer type is the first step to knowing whether an offer is low, fair, or genuinely top-tier for Colombia.

How company tier reshapes your compensation

Two offers can both say “Senior ML Engineer - Bogotá - COP 220M+” and still live in completely different worlds once you look at who’s paying. In Colombia, your total compensation is shaped as much by company tier as by your skills: a senior AI profile doing similar work can earn under 120M in a traditional enterprise, or push beyond 350M-450M total comp in a global tech hub with RSUs and bonuses.

At the top, Tier 1 multinationals - think Microsoft, Google, AWS, Mercado Libre and some advanced teams inside IBM, Accenture and Globant - tend to benchmark Bogotá and Medellín against global “emerging market” bands. For L5+ AI and ML engineers, that usually means around 250M-350M+ base, with total compensation often reaching 350M-450M+ once you add annual bonuses and USD 5k-40k per year in RSUs that vest over four years. Sign-on bonuses in the 20M-60M range are common for in-demand profiles.

Tier 2 national leaders - banks like Bancolombia and Grupo Aval, insurers such as Sura, large telcos and retailers, plus Rappi’s more corporate-style units - usually run on stricter salary grids. Senior Data Scientists and ML Engineers often land between 100M-150M base, with only exceptional cases nudging toward 180M. Bonuses tend to be predictable but modest: roughly 1-2 months of salary for good performance, and equity is rare outside a few tech-native firms. Public ranges for data roles at large Colombian corporates on sites like Glassdoor’s Bogotá salary reports align closely with these bands.

Then there’s Tier 3: startups and boutique AI/nearshore shops (Factored, DeepSea, PSL/Perficient, and similar). These firms often pay mid-senior engineers with strong English in the 150M-250M base range and can edge into Tier 1 territory when competing for rare MLOps or LLM talent. Instead of RSUs, they lean on stock options or phantom shares, whose real value depends entirely on future exits. In practice, that means a “Senior ML Engineer - 200M” title can be under-market in Tier 1, generous in Tier 2, or just the starting point in a venture-backed startup.

Understanding which tier you’re talking to changes how you read the offer. In Tier 1, the game is levelling and RSUs. In Tier 2, it’s pushing to the top of the band and securing strong bonuses and learning support. In Tier 3, you trade stability for upside, weighing a slightly higher base and genuine equity potential against the risk that those phantom shares never pay out.

Mapping Colombian titles to international levels

On Colombian LinkedIn, titles can be deceptive. A “Senior Data Scientist” in a mid-size Bogotá retailer may actually be operating at what a US company would call L3 or L4, while an “ML Engineer” at a cloud provider in Medellín might be doing true L5 work with regional impact. To compare offers fairly - especially if you’re targeting remote roles with US or European employers - you need to translate local titles into the international L3-L6 ladder used by most big tech companies.

A practical way to do this is to ignore the label and focus on three things: scope (how big are the systems and teams you influence), independence (how much supervision you need) and business impact (are you moving a feature, a product, or a whole line of business). Years of experience are a guide, but Colombian careers often move faster in startups and slower in large enterprises, so treat them as ranges, not rules.

Roughly, most Colombian AI titles map to international levels like this:

• Junior Data Scientist / ML Engineer Junior: L3, about 0-3 years; you implement models and pipelines with close guidance and own well-defined pieces of work.
• Data Scientist / ML Engineer / AI Engineer: L3-L4, around 2-5 years; you deliver features or models end-to-end and may mentor interns or juniors.
• Senior Data Scientist / Senior ML Engineer: L4-L5, roughly 4-8 years; you lead projects, design architectures and coordinate with product, operations and business stakeholders.
• Tech Lead Data / Lead ML Engineer / MLOps Lead: L5, usually 6-10 years; you own critical systems, guide squads and influence technical roadmaps.
• Principal / Staff / Distinguished Engineer: L6+, often 8-12+ years; you shape company-wide AI strategy and advise on large investments in platforms and teams.

To sanity-check where you really sit, compare your responsibilities and impact with international profiles and compensation curves on platforms such as PayScale’s breakdown of Colombian data roles, then cross-reference with what global L3-L6 job descriptions expect. That translation is what lets you argue convincingly for a higher level - and pay band - when you talk to a Bogotá unicorn, a Medellín nearshore shop, or a US-based remote employer.

Anatomy of a Colombian AI compensation package

When you see “COP 220M/year” on an offer, you’re only looking at the facade. Inside a Colombian AI compensation package are layers of base pay, benefits, social security, taxes and sometimes equity that can easily shift your real income by tens of millions of pesos per year. Understanding each layer is what lets you compare a banco in Bogotá with a startup in Medellín or a nearshore consultancy serving US clients.

Almost every offer you’ll see has the same core pieces:

• Base salary: quoted monthly in contracts, even if recruiters talk in annual figures. For high earners, this is often offered as salario integral.
• Variable pay: performance bonuses or profit-sharing, often worth around one or two extra monthly salaries in large enterprises if targets are met.
• Equity: RSUs in global tech companies, or stock options/phantom shares in startups and nearshore AI boutiques.
• Legal benefits (prestaciones sociales): cesantías, intereses de cesantías, prima de servicios and paid vacations when you are on a non-integral salary.

On top of this, Colombia’s social security system takes a predictable slice. Employees typically contribute about 8% of their salary, split between 4% health (EPS) and 4% pension, while employers add roughly 20-30% extra in costs for pension, health, ARL (work-risk insurance) and the caja de compensación when the contract is not integral. Guides aimed at foreign employers, such as Niural’s overview of hiring in Colombia, emphasize how these contributions significantly increase the true cost of a senior AI hire beyond the quoted base.

Income tax then adds another layer. Colombia uses a progressive system with rates climbing up to 39%; AI professionals earning above roughly 200M/year tend to reach 33-35% marginal brackets quickly, depending on deductions like voluntary pensions or AFC savings. At senior levels, this makes the structure of your package almost as important as the nominal amount: the difference between salario integral and traditional contracts, or between COP-based bonuses and foreign-currency RSUs, can easily decide whether a role in Chapinero, El Poblado or fully remote actually leaves you with more money at the end of the year.

Finally, many Colombian tech and AI employers add perks that don’t appear in the base number but matter in practice: private health upgrades, food or transport stipends, home-office or internet subsidies, and annual education budgets for courses, certifications and conferences. For AI and ML roles, explicit support for cloud credits and GPU access is especially valuable, because it directly affects your ability to build the portfolio and production experience that unlock higher compensation bands later on.

Real offer examples and net take-home estimates

Two offers can both advertise “COP 140M-280M/year” and feel generous, but your net take-home can differ by nearly COP 80M depending on contract type and tax bracket. Walking through concrete examples is the easiest way to see how Colombian social security and income tax reshape AI salaries in Bogotá and Medellín.

Consider a mid-level Data Scientist (L4, 4-6 years) at a Tier 2 bank in Bogotá on a traditional (non-integral) contract with a base of 140M/year, or about 11.67M/month:

• Employee social security (health + pension) at roughly 8% of salary: ≈ 0.93M/month.
• Net after social security: 10.74M/month.
• Assume an effective income tax rate of around 15% at this level: ≈ 1.75M/month in tax, using the gross base as a simple reference.
• Very rough monthly net: ≈ 9.0M, or about 108M/year in cash flow.

Because the salary is non-integral, primas, cesantías, intereses de cesantías and vacation pay are additional, effectively pushing the real annual net above that 108M figure.

Now compare that with a Senior MLOps Engineer (L5, 7-9 years) at a Tier 3 AI startup in Bogotá on a salario integral of 280M/year, or about 23.33M/month:

• Employee social security (again around 8%): ≈ 1.87M/month.
• Net after social security: 21.46M/month.
• At this income, effective income tax often lands near 25% of gross: ≈ 5.83M/month.
• Approximate monthly net: ≈ 15.6M, or about 187M/year in take-home.

Here, all legal benefits are already “baked into” the integral figure, so there are no extra primas or cesantías on top. The offer might also include phantom shares, which can end up being worth nothing or a substantial bonus depending on the startup’s exit. When you contrast these numbers with the significantly higher USD-paid remote rates documented for Colombian AI engineers in reports like Expand’s guide to hiring AI engineers in Colombia, the importance of modeling your net pay becomes obvious: you want to compare apartments by total monthly cost, not just the sticker price in the listing.

Salary percentiles: where you sit in the market

Percentiles are the simplest way to see where you stand in Colombia’s AI economy. Instead of asking “Is 180M good?”, you ask “Am I being paid like someone at the 25th, 50th or 75th percentile for my role and level?”. Regional analyses of AI engineers in LatAm, such as RemotelyTalents’ 2026 salary comparison, consistently show a wide spread between local-tier and export-tier talent - and Colombia is no exception.

For mid-level (L4, 4-6 years) AI professionals across Colombia, typical annual base salary percentiles look like this:

• ML Engineer: 25th ≈ 130M, median ≈ 170M, 75th ≈ 210M.
• AI Engineer: 25th ≈ 120M, median ≈ 160M, 75th ≈ 200M.
• Data Scientist: 25th ≈ 110M, median ≈ 135M, 75th ≈ 160M.
• MLOps Engineer: 25th ≈ 140M, median ≈ 180M, 75th ≈ 220M.
• AI Researcher: 25th ≈ 150M, median ≈ 190M, 75th ≈ 230M.
• Applied Scientist: 25th ≈ 160M, median ≈ 205M, 75th ≈ 250M.

At the senior level (L5+, 7+ years), percentiles shift sharply upward as you move into Tier 1 multinationals and top-tier startups:

• ML Engineer: 25th ≈ 220M, median ≈ 270M, 75th ≈ 320M, 90th ≈ 370M+.
• AI Engineer: 25th ≈ 215M, median ≈ 265M, 75th ≈ 315M, 90th ≈ 365M+.
• Data Scientist: 25th ≈ 170M, median ≈ 210M, 75th ≈ 250M, 90th ≈ 300M+.
• MLOps Engineer: 25th ≈ 240M, median ≈ 290M, 75th ≈ 350M, 90th ≈ 400M+.
• AI Researcher: 25th ≈ 260M, median ≈ 320M, 75th ≈ 380M, 90th ≈ 430M+.
• Applied Scientist: 25th ≈ 280M, median ≈ 340M, 75th ≈ 420M, 90th ≈ 450M+.

In practice, sitting around the 25th percentile often means a national enterprise outside the core tech sector or a role that’s mislabeled as “senior.” The 50th percentile usually lines up with high-growth local startups and solid nearshore firms. Breaking into the 75th-90th percentiles typically requires a mix of scarce skills (MLOps, LLMs, GenAI), strong English and landing in a Tier 1 multinational or a top-tier Medellín/Bogotá startup that sells into US and European markets.

Negotiation tactics tailored to Colombian employers

Negotiating in Colombia isn’t about being aggressive; it’s about understanding how local employers think about bands, levels and risk. Before you discuss numbers, get clear on your priorities: aim first to secure the right level and base salary, then fine-tune bonuses, equity and remote flexibility. For most AI roles, the order tends to be: base pay, level/title, location (Bogotá vs Medellín vs remote), then long-term upside like RSUs or stock options.

Go into any conversation with concrete evidence. Collect at least three recent offers or public ranges for similar roles in Bogotá/Medellín and write down a target band and a walk-away floor. When the recruiter asks for expectations, reply with a bracket tied to value, not wishful thinking: “For a role with this scope and seniority, I’m targeting a package in the upper range of what similar positions pay in the Bogotá market, something like between X and Y, depending on level and benefits.” This framing mirrors how international firms already think about compensation, and aligns with the skills-focused mindset described in PwC’s analysis of AI and work.

With Tier 1 multinationals (cloud providers, global product companies, large nearshore centers), your main lever is level. Ask early: “Which global level is this mapped to?” and “What would have to be true for this to be considered at the next level up?”. If they won’t move on level, pivot to the long-term components: ask whether there is flexibility on equity grants, signing incentives or refresh cycles, and make sure you understand vesting schedules before accepting a slightly lower base in exchange for stock.

In Tier 2 enterprises (banks, telcos, traditional corporates), salary bands are rigid. Your best moves are:

1. push to the very top of the band by demonstrating impact in revenue, risk or efficiency;
2. negotiate clearer performance criteria and bonus multipliers;
3. secure paid time and budget for upskilling in MLOps and LLMs. For Tier 3 startups and nearshore boutiques, explicitly separate “cash today” from “future upside.” Ask for the exact equity percentage on a fully diluted basis, vesting and exit expectations, then state your minimum acceptable base: “Given the risk level, I’d need at least X in fixed compensation, and we can use equity to close any remaining gap.”

When equity is worth the trade-off

Equity is the part of your offer that never shows up in the LinkedIn salary filter but can quietly turn a “normal” Bogotá or Medellín package into a life-changing one. The catch is that not all equity is created equal: RSUs in a listed company like a global cloud provider or a regional giant such as Mercado Libre behave very differently from stock options in a private startup, and both are miles away from the phantom-share schemes common in Colombian SAS structures.

In large, publicly traded tech companies, RSUs are usually the safest form of equity. They convert into real shares on a fixed schedule, and you can look up the market price on any trading day. For senior AI roles, it’s common for a meaningful slice of total compensation to arrive this way, which is why many multinationals use stock to compete in the global war for AI talent that outlets like Forbes’ AI trend reports describe. In that context, accepting a slightly lower base can make sense if the RSU grant is large, the company is stable, and you plan to stay long enough to vest most of the grant.

Startup equity in Bogotá or Medellín is a different game. Options or phantom shares in a Rappi supplier, a Ruta N spin-off, or a nearshore AI boutique only pay out if there’s a liquidity event: acquisition, IPO or a structured buyback. Many never reach that point. On the other hand, well-funded startups with growing revenue and international clients sometimes use generous option pools to close the gap with US-level offers; it’s how some senior AI developers in the region have quietly crossed the USD 100k+ total-compensation line while still living in Colombia.

• Equity is usually worth a base-salary trade-off when it’s in a publicly traded company with transparent pricing and you’re receiving a significant grant.
• It can be worth a smaller trade-off in a late-stage startup (series B/C+, clear growth, institutional investors) where you know your fully diluted percentage and vesting terms.
• It’s rarely worth sacrificing more than a modest amount of guaranteed cash for early-stage or vague phantom-share schemes without clear exit scenarios, no matter how exciting the pitch deck looks.

The practical approach for Colombian AI professionals is to treat equity as a measurable asset, not a dream. Ask for the grant value in writing, run your own scenarios, and remember that in cities like Chapinero or Laureles, rent, loans and family obligations are paid in COP today, not in hypothetical stock tomorrow. Used wisely, though, equity is one of the few levers that can let a Bogotá or Medellín salary play in the same league as offers in much richer markets.

Bogotá versus Medellín: salary, cost and strategy

Choosing between Bogotá and Medellín as your AI base is a bit like choosing between two similar-looking towers with very different admin fees. On paper, both cities host serious employers and strong university pipelines; in practice, salaries, living costs and lifestyle pull you in different directions. Bogotá usually offers a clear “capital premium” on AI pay, especially in financial services and global consulting, while Medellín trades a slightly lower average salary for cheaper rent, shorter commutes and a more relaxed day-to-day rhythm.

In Bogotá, concentration is the keyword. Around the financial corridors and the north of the city you’ll find data and AI teams for Bancolombia, Grupo Aval, Ecopetrol, Rappi, Mercado Libre, Globant, IBM, Accenture and multiple nearshore consultancies. That density of high-budget employers is what pushes many mid- and senior-level AI roles into the upper local bands. Medellín’s ecosystem is smaller but highly focused: Ruta N, EAFIT, UPB and a cluster of firms like PSL/Perficient, Endava and Globant anchor a nearshore-heavy market where work for US and European clients is common, but nominal COP salaries often sit about 5-10% lower than Bogotá for similar roles.

Where Medellín fights back is on cost and quality of life. Rents in Laureles or parts of Belén can be substantially below comparable neighborhoods in Chapinero or Cedritos, and daily expenses - transport, eating out, services - tend to stretch your pesos further. For AI professionals working remote-first roles, that gap effectively acts as a built-in raise: keeping a Bogotá or international salary while living Medellín-style creates the room to save, invest or simply work less overtime without losing financial ground.

The smartest strategy many Colombian AI engineers now follow is hybrid: build your network and early experience in Bogotá’s dense corporate and multinational scene, then leverage that track record to secure fully remote or nearshore roles that pay off regional or US/EU benchmarks while basing yourself wherever your life runs best. Global discussions on how AI is reshaping careers, like the World Economic Forum’s analysis of AI and talent strategies across industries, underline the same point: geography is becoming a choice, not a cage, especially for scarce skills in ML, MLOps and LLMs.

If Bogotá is the tower with higher admin fees but more elevators, Medellín is the slightly shorter building where the utilities are cheaper and the rooftop is always open. Your job is to decide whether you want to climb faster, live better - or design a path that lets you do both.

Upskilling and Nucamp pathways to higher pay

Moving from the 25th to the 75th or 90th percentile in Colombia’s AI market is rarely about “being paid more for the same work.” It usually comes from adding scarce skills: solid Python and SQL, production-grade MLOps, and now hands-on experience with LLMs and GenAI. Global analyses of how AI is reshaping jobs, like PwC’s predictions on AI and talent, emphasize exactly this shift toward hybrid “AI generalists” who combine technical depth with business impact.

For professionals in Bogotá and Medellín, that means building a concrete learning plan, not just watching random YouTube tutorials between sips of tinto. Employers from Rappi and Mercado Libre to nearshore firms in Ruta N are screening for proof that you can ship real systems, not just talk about models. That’s where structured programs like Nucamp become useful: an international online bootcamp with local communities in cities like Bogotá, Medellín and Cali, offering AI and coding paths at prices that fit Colombian salaries.

Nucamp positions itself as one of the most affordable AI bootcamp options, with core programs between COP 8,496,000 and COP 15,920,000, flexible payment plans, and community-based learning in 200+ cities worldwide. Outcomes data show an employment rate around 78%, a graduation rate near 75%, and a 4.5/5 Trustpilot rating from roughly 398 reviews, with about 80% five-star feedback. For someone earning 60M-80M/year today in a non-AI role, reaching even the 123M-170M bands that mark the 25th-50th percentiles for AI roles in Colombia can pay back that tuition in roughly a year - especially if you’re based in Medellín and capturing Bogotá or international-level salaries.

Checklists and an action plan to move up a band

Knowing that you’re underpaid is one thing; turning that into a concrete plan is another. Think of this as moving from one floor of the building to the next: you need to know where you stand today, which elevator to take (skills, projects, employer change), and how you’ll negotiate once you get there. A simple set of checklists can keep you honest and focused over the next 6-18 months.

Start by auditing your current position against the Colombian market, not just your empresa:

• Role clarity: Is your day-to-day closer to data analysis, ML engineering, MLOps, or applied research?
• Level reality check: How many people do you mentor? What systems do you own end-to-end? How big is the business impact you can quantify?
• Compensation: Compare your base, bonuses and equity with at least three external data points for similar roles in Bogotá or Medellín, using regional salary guides like igmguru’s AI engineer salary overview as a directional reference.
• Company tier: Label your employer as Tier 1, 2 or 3 based on its size, market and client base.

Once you know roughly which percentile you’re in, translate that into a 6-12 month skill plan:

• Pick one “vertical” skill: deeper ML, LLMs/GenAI or experimentation.
• Add one “horizontal” skill: MLOps tooling, cloud infrastructure, or data engineering.
• Define 2-3 portfolio projects that prove these skills in production-like conditions (APIs, monitoring, CI/CD, real datasets).
• Commit to one structured program (university course, bootcamp, or certification) instead of scattered tutorials.

The final piece is your 12-18 month career move plan:

• Decide whether your next jump is inside your current employer (promotion/level change) or outside (tier upgrade or remote role).
• Schedule quarterly check-ins with yourself: “Have I shipped a new project? Did I expand my scope? Did I speak at a meetup or contribute internally?”
• Build a short, quantified impact document before every performance review or interview: metrics, before/after charts, and clear descriptions of what you owned.
• Keep an updated target list of 10-15 employers across Bogotá, Medellín and remote-friendly companies, and apply only when your portfolio matches the level you’re aiming for, not just when you see any “AI Engineer” title.

Follow this rhythm and you stop hoping for a raise and start running a deliberate campaign: upgrade skills, prove them in code, move to a higher-paying tier or level, then negotiate from evidence instead of emotion. Over a couple of cycles, that’s how Colombians in Chapinero, Cedritos, Laureles or El Poblado quietly climb from “grateful to be in tech” to sitting comfortably in the upper floors of the AI salary tower.

Frequently Asked Questions