AI in Trading Is Growing Fast—But Who’s Building the Algorithms?

But the most important question is: who is truly making the algorithms that are making this AI revolution happen?

There are teams of people—data scientists, financial analysts, compliance officers, and engineers—working around the clock to make sure that the algorithms that power the sleek trading platforms and automated decision-making not only operate well but also stay safe, legal, and ethical.

In this article, we'll speak about the people that work behind the scenes on AI trading, the challenges organizations are having because they don't have enough experienced individuals, and how more and more companies are leveraging global offshore teams to create and improve their AI skills.

The Rise of AI in Trading

Algorithmic trading isn’t new. Hedge funds and investment banks have been using computer models to guess what will happen in the market and make bets for decades. But the speed has picked up a lot because of improvements in cloud computing, big data, and machine learning.

According to Fortune Business Insights, the global algorithmic trading market will expand from $14.9 billion in 2021 to $31.1 billion by 2028, with a compound annual growth rate (CAGR) of 11.1%.

AI-driven hedge funds have already done better than traditional funds on a number of benchmarks. This shows how automation can give you an edge in the market.

Retail investors are getting in on the action as well, thanks to AI-powered apps that give them access to tools that were once only available on Wall Street.

What is causing this growth? There is a lot of data, computers are getting quicker, and there is more pressure to uncover alpha (returns that outperform the market).

The Myth of “Fully Automated” Trading

People typically think of a future when robots make all financial decisions without any human help when they hear the term "AI trading."

What is the truth? AI in finance is not hands-off at all.

Humans design and train the models, while algorithms carry out deals.

• Choose the data sets that the system will use.
• Understand results and make plans more precise.
• Keep an eye on compliance to prevent breaking the rules.

Think about the Flash Crash of 2010. A trading algorithm made the market drop quickly and lose roughly $1 trillion in value in only a few minutes. The experience proved that automation can go too far and that people still need to be in charge.

AI can be quick, but people are still the ones that make the machines operate.

Who’s Actually Building Trading Algorithms?

Let's look at the main jobs that make AI-powered trading platforms work.

1. AI Engineers and Data Scientists

These are the people that create and put into action machine learning models. 

• They make models that can predict the future using past market data.
• Test strategies in different situations (backtesting).
• Make models work better such that risk and return are in balance.

2. People who study finances

AI, no matter how advanced, still requires people who know about money. Analysts give: 

• Market context to stop overfitting, which is when models function on data from the past but fail in real time.
• Changes that are distinctive to a domain that machines can't "guess."
• Working with AI teams to add human insight to the code.

3. Compliance and Risk Officers

In finance, rules are as critical as returns. Compliance experts:

• Ensure trading algorithms meet SEC, FCA, MiFID II, and other regulations.
• Monitor transactions for suspicious activity or unintended risks.
• Set up ethical guidelines around market manipulation and fairness.

4. Engineers for DevOps and MLOps

Making an algorithm in a lab is different from making one in real life. It's a different story to make it work for live trade. Engineers:

• Make sure that algorithms can operate 24 hours a day, seven days a week, without stopping.
• Manage deployment pipelines to keep models up-to-date.
• Keep an eye on performance in real time to avoid costly mistakes.

The Talent Bottleneck: Why AI in Finance Isn’t Scaling Fast Enough

Here’s the catch: while demand for AI-driven trading is exploding, the talent pipeline is struggling to keep up.

• The U.S. alone has over 163,000 unfilled data science and analytics roles (U.S. Bureau of Labor Statistics).
• A Deloitte survey found that 61% of financial services firms cite “talent shortages” as their biggest barrier to AI adoption.
• Competition is fierce—hedge funds, banks, and fintech startups are all hiring from the same limited pool.

Hiring a senior data scientist in New York or London can cost well over $150,000 annually, not including benefits and bonuses. For startups and small firms, these costs are often prohibitive.

Without enough talent, many firms risk falling behind competitors who are scaling their AI capabilities more aggressively.

The Global Workforce Behind AI in Trading

This is where global talent strategies are changing the game.

Firms are increasingly looking beyond their home countries to build offshore and hybrid AI teams.

• Fintech startups are hiring offshore developers in Asia and Eastern Europe to cut costs while maintaining high skill levels.
• Global banks are setting up compliance and monitoring teams in the Philippines and India for round-the-clock oversight.
• Hedge funds are tapping into offshore analytics talent to process vast streams of market data.

By going offshore, companies get:

• Access to specialized talent pools.
• Lower staffing costs (often 50–70% less than hiring locally).
• Time-zone advantages, enabling 24/7 operations.

This doesn’t just benefit large institutions—startups can now compete with Wall Street giants by leveraging offshore data science and engineering talent at a fraction of the cost.

Offshore Data Science: A Competitive Advantage

Let’s take a closer look at why offshore staffing works particularly well for algorithmic trading and finance-related AI.

• Abundant Talent: Countries like the Philippines, India, and Poland are producing thousands of skilled data science graduates each year.
• Strong English Proficiency: Communication barriers are minimized, making collaboration seamless.
• Cost Efficiency: Firms save significantly on salaries, infrastructure, and overhead.
• Scalability: Offshore staffing allows businesses to ramp teams up or down quickly based on project needs.

For startups, this model is often the only way to level the playing field against billion-dollar hedge funds. A recent analysis of offshore staffing providers, including companies like Kineticstaff, TopTal, and Upwork Enterprise, shows that small and mid-sized businesses can achieve 60-70% cost savings while accessing specialized talent pools without the overhead of building offshore operations from scratch. The study found that 78% of fintech startups using these services successfully scaled their AI capabilities within the first year.

Addressing the Concerns: Security and Compliance

Skeptics often raise a valid concern: can offshore teams handle sensitive financial data safely?

The answer is yes—when structured correctly. Many outsourcing providers implement enterprise-grade security standards, including:

• ISO 27001 certification for information security management.
• SOC 2 compliance for data handling practices.
• GDPR adherence for European data protection standards.

Additionally, firms often use a hybrid model:

• Onshore teams manage sensitive data and regulatory approvals.
• Offshore teams handle analytics, development, and monitoring.

This combination ensures security while still unlocking offshore advantages.

The Future of AI Trading Talent

Looking ahead, the demand for human expertise in AI trading will only grow.

• By 2030, most financial firms are expected to run hybrid teams that blend onshore oversight with offshore execution.
• New roles will emerge, focusing on AI ethics, regulatory compliance, and risk monitoring.
• AI won’t replace humans—it will reshape jobs, moving people into higher-value oversight and strategy positions.

The firms that embrace this model early will be better positioned to innovate, adapt, and stay competitive in a market where speed and intelligence are everything.

Key Takeaways for Business Leaders

• AI in trading is exploding, but algorithms don’t build themselves—people do.
• Talent shortages in AI and data science are slowing progress, especially in Western markets.
• Offshore staffing offers a scalable, cost-effective solution to access specialized talent.
• Security and compliance risks can be managed with the right frameworks.
• The future of trading will rely on global teams that combine local oversight with offshore execution.

Conclusion

The rise of AI in trading is often framed as a battle between humans and machines. But the truth is far more collaborative: AI may power the trades, but it’s people who power the AI.

For financial firms, fintech startups, and hedge funds alike, the next competitive advantage won’t come from algorithms alone. It will come from how well they build, manage, and scale their human workforce—increasingly through global offshore talent.

By rethinking workforce models today, firms can ensure they’re not just keeping pace with the AI revolution but leading it.