The adoption of AI by Fintech companies has inevitably led to increased competition for AI talent to design and implement AI/ML/LLM-driven models, tools, workflows, and agents. 

This demand has evolved rapidly from data scientists/machine learning engineers working on simple automation systems to applied AI engineers/AI solution architects working on productionising AI and solving specific business problems: fraud detection and prevention; credit scoring, algorithmic trading; personalised customer support; document analysis, financial workflow automation; complex risk management etc. 

The Competition for AI Talent

Fintech start-ups and scaleups not only have to compete with each other for AI talent, but they also compete with large financial services firms with huge AI budgets, hedge funds and private equity firms.

A highly sought-after target group are AI engineers with 2 plus years’ experience in big tech or frontier labs – Google DeepMind, OpenAI, Anthropic, Microsoft Research/AI Platform, Meta Reality Labs, Amazon AGI/AWS AI Labs, Apple ML Research - plus an MSc or PhD in Computer Science. 

This group are in a strong position. The big tech companies offer top-tier salaries, equity and bonuses, as well as the knowledge that working for these firms will offer access to the top compute and have a large-scale impact.

Many financial services firms and hedge funds have deep hiring budgets and can outbid big tech by offering strong base salaries and/or higher bonus potential. Fintech startups or scale-ups unable to compete on base salaries need to effectively communicate:

• their mission,
• a strong equity-growth story
• the chance to make a meaningful impact from day one

Sourcing AI Talent

For many Fintechs, AI/ML/data skills combined with regulatory, risk, compliance, and financial modelling experience is deeply valuable and can be sourced from a wide range of companies employing AI specialists:

• Traditional financial institutions. These large banks, insurance companies, payment processing, credit bureaus and trading firms are upskilling and modernising to develop in-house AI skills
• Non-tech domains, including healthcare, telecom, retail and insurance, which utilise AI at scale and employ engineers with production experience - MLOps, fraud detection, data analytics, personalisation, recommendation engines, risk modelling. This applied AI talent is often more highly valued than research engineers from frontier labs for its expertise in shipping AI into regulated production.
• Firms serving the financial services sector: cybersecurity, credit scoring, fraud detection, fintech AI infrastructure and SaaS companies. 
• Universities and AI bootcamps.  University AI research labs work at the intersection of industry and academia and produce MSc/PhD level, deployment-ready AI engineers. 

Challenges Facing Fintech

Vague role definition - the quality of a job description needs to be well thought out, focussed and detailed. ‘AI Engineer’ can apply to an array of different roles. Firms seeking a founding AI Engineer into the company may be tempted to bundle a long list of data engineering, generative AI, agentic AI and software engineering responsibilities into the same position, which will be off-putting for potential candidates and shows uncertainty in the boundaries of the role and business impact.

Salary and benefits ambiguity - ‘competitive’ salaries for AI positions in Fintech are common. For senior and leadership roles where the total compensation package comprises base salary, bonuses, equity, other incentives and possibly intellectual property ownership, this needs to be clear from the outset to show how rewards scale with company growth.

Speed of decision making - slow progress through the recruitment process (which could include too many interviews and skills-based assessment steps), will result in strong candidates dropping out. Experienced AI professionals, even in a cool labour market, will have options. Ideally, the E2E hiring process (1st to final interview) should be less than 10 days. 

Effective CV/interview assessment - without in-house AI expertise it is difficult for Fintech firms to evaluate CVs and know when they have received a strong application.

RWA is well placed to assist with salary benchmarking, advice on job descriptions, sourcing top-flight candidates, and effectively screening and interviewing applicants for AI roles.

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