Building an AI Development Team: A Guide for Financial Services

If you’re a financial services firm trying to build an AI development team, chances are you have a project that needs to get underway urgently. However: 

• You’re uncertain where to start in building a team from scratch or ramping up a small team.
• Your current team may not yet have the skills, capacity, or understanding to manage AI projects independently, define success metrics, or ensure compliance.
• You’re unsure whether to build an AI development team in-house or work with an external team instead.

At Neurons Lab, we’ve delivered over 100 successful AI projects, helping financial institutions like Visa, AXA, PrivatBank, and dAppForge embed AI into their operations in tailored, compliant, and scalable ways. In that time, we’ve co-developed solutions and guided countless teams, seeing firsthand what works and what doesn’t when building AI capability inside financial organizations.

In this article, we cover:

• What are the Key Roles Needed in an AI Development Team?
• What to Consider when Building an AI Team for Financial Services
• When to Build a Team In-House or Partner Externally (or Both)
• How Neurons Lab Helps You Build an AI Development Team 
• How A Leading Asian Bank Grew RM Client Assets by 30% with Neurons Lab
• Why Choose Neurons Lab To Build Custom AI Teams and Solutions for Financial Services
• FAQ

Want to get started building your AI development team? Neurons Lab can help. Book a call with us today.

What’s the Right Structure and Key Roles for an AI Development Team in Financial Services? 

When hiring, some firms make the mistake of focusing solely on hiring AI engineers. However, delivering production-grade AI systems requires a mix of technical and business-focused roles that work in sync.

While AI projects involve building models, they also go beyond this to include deployment, scalability, and alignment with business objectives and regulatory compliance standards. That’s why successful teams pair technical expertise (AI, software, and cloud engineers) with product and leadership roles that translate business needs into clear technical requirements.

As projects mature, so should team structure. Early-stage projects might merge roles like software and cloud engineering into one, while larger, scaling teams need more defined responsibilities and stronger alignment between technical delivery and business outcomes. 

From experience, we’ve found that high-performing AI development teams include the following key roles:

AI Engineer

An AI engineer builds the intelligent agents or machine learning models (e.g., chatbots, computer-vision models) that perform complex tasks. They design and train models and refine algorithms so the AI can understand data and perform complex tasks autonomously. They also prepare datasets, test performance, and collaborate with other engineers to integrate artificial intelligence into new or existing products or services (e.g., banking or insurance apps or customer portals). Their goal is to turn an AI concept into a measurable, working system.

Software Engineer

A software engineer makes the agents built by the AI engineer usable in real applications, like a chatbot inside a bank’s customer-facing online portal or mobile app. They integrate it into existing systems (e.g., core banking, claims, CRM) ensuring smooth, secure, and scalable performance.

Infrastructure Engineer or Cloud Engineer

The infrastructure engineer or cloud engineer handles deployment and scaling. They connect sandboxed agents or models with financial services applications, effectively putting the AI system on cloud rails. They ensure availability and stability, so that firms can scale a solution reliably.

Technical Lead or AI Architect

The technical lead or architect is responsible for bringing all the technical components together, from AI models and application code to infrastructure, so they work as a unified system. They coordinate the work of the engineering team, make key architectural decisions, and ensure technical quality and scalability. 

MLOps Engineer

An MLOps engineer manages the operational layer and data pipelines that keeps machine learning systems reliable, traceable, and measurable. They monitor and maintain live AI systems to track every action, decision, and interaction, ensuring consistent performance and accountability.

In regulated industries like financial services, this role is critical for maintaining auditability and guaranteeing that AI decisions can be explained and trusted. 

Once the technical foundation is in place, the effective leadership and coordination of the next two roles help deliver AI solutions efficiently, meeting business goals and compliance.

Project Manager 

While technical leads or architects usually focus on system design and engineering quality, a dedicated project manager is essential to coordinate delivery and align technical work with your business goals (e.g., integrating a chatbot into a customer service app). Their responsibilities consist of managing timelines, budgets, and resources to keep the project on track and within scope. 

AI Product Manager 

An AI Product Manager also acts as a bridge, connecting the technical side (engineers) with the business side (clients, users, and leadership). They make sure your AI solutions solve real problems like detecting fraudulent transactions, deliver measurable value (e.g., reducing claims processing time by 30%), and can be maintained and improved over time. Product managers also define and track success metrics to ensure continuous performance and alignment with business goals.

What to Consider When Building an AI Team for Financial Services

We’ve seen financial services firms run into common obstacles in building a development team, such as internal conflicts and a lack of internal metrics, that often slow down AI projects. Here are a few tips to help you overcome hurdles so you can form a team capable of delivering and maintaining successful AI solutions.

Set Up the Right Governance to Ensure your Teams Can Track, Measure and Improve AI Systems

AI projects often lack proper governance and are treated like research experiments or proofs of concept, without clear metrics or defined targets.

Unlike traditional software development where targets are clear and measurable, AI development is different. For example, software development teams can set goals like updating a mobile app’s features within two days or setting up a zero bug policy in production. 

These issues are straightforward to identify and fix. If a button doesn’t work, you log a ticket, and a software engineer can resolve it in a matter of hours or minutes. The effort can be estimated, tracked, and managed easily. 

However, systems like AI agents are complex and “non-deterministic”, meaning an issue can have multiple underlying causes. Fixing it can’t be tied to a specific timeframe and requires deeper technical analysis to understand what happened during a user interaction.

Take a wealth management AI chatbot that misunderstands a client’s question or gives the wrong advice. To pinpoint where it went wrong, teams need full transparency into the client’s question, how the agent searched the database, the options it considered, and the API calls it made. 

This is where strong governance, delivered via a dedicated AI operations layer (MLOps or AIOps) comes in. This operational layer acts as a system of control, defining ownership, decision-making, escalation paths, and performance standards. It also provides the traceability, monitoring, and measurement needed to diagnose issues and implement fixes.

With this foundation in place, teams can manage the entire AI development lifecycle and continuously improve AI performance, ensure compliance, and keep projects aligned with business goals. 

AIOps combines monitoring, automation, and engagement to turn real-time and historical data into actionable insights. It supports predictive analytics, anomaly detection, and performance monitoring to make AI systems measurable and reliable. Image Source: LeewayHertz

Set Realistic ROI Expectations to Guide AI Team Performance

Some financial services leaders still evaluate AI initiatives using the same metrics as traditional digital projects like speed, cost savings, and short-term ROI. But AI operates on different economics. It requires continuous learning, data management, and model upkeep, all of which add operational costs while creating new, harder-to-quantify forms of value.

For instance, a voice agent may cost more to maintain than a web app, yet it can serve customers with visual impairments who would otherwise be excluded. The margins may be smaller, but the extended reach and accessibility open new market segments and strengthen long-term brand value.

When executives understand that AI produces value through new capabilities and not just via cost reduction, they can set more realistic performance goals for their teams. This clarity reduces pressure for short-term ROI and allows AI teams to focus on building reliable systems and defining meaningful ways to measure performance for long-term, scalable impact.

Define New Quality Standards to Keep AI Performance Consistent

Once expectations are set, the next challenge is defining what success actually looks like in AI.

Teams often struggle to measure success or define what ‘good’ looks like in AI systems. With regular software, an application either works or it doesn’t. You can tell right away if a desired outcome was achieved, such as a payment was scheduled or a transaction failed.

However, with AI, results are subjective. Unlike traditional products with clear-cut outcomes, AI systems produce non-deterministic results that change with context. That makes it harder to measure performance, maintain consistency, and keep customers satisfied.

For example, a chatbot response might be too long, too short, or inconsistent with company style. Improving its performance requires full traceability of the AI agent’s decision path and well-defined quality criteria such as what exactly it means to follow the company style. 

This responsibility often falls to AI product managers, who must translate vague user feedback into measurable technical standards. Ultimately, teams will need to move away from binary definitions of success and establish clear, context-based criteria for measuring performance.

An example of evaluating AI impact Source: LinkedIn

Align Product and Technical Teams to Speed Up Project Delivery

AI projects often lose momentum when product and technical teams have different priorities. For instance, a Head of Claims Reclamation might need an AI-driven solution to automate claims processing, but the technical team may be juggling other projects and lack the capacity to deliver quickly.

Strong alignment starts at the leadership level as product, tech, and business heads need shared goals and metrics from day one. 

To keep initiatives on track, many executives bring in experienced AI partners to align teams and speed up delivery. While valid, this solution can also create tension and pushback. Internal teams may feel their work is being replaced or that control over the project and their budget are being taken away.

The most effective approach is to align external and internal teams through a co-development model. External experts add capacity, guide implementation, and upskill your teams along the way, ensuring that, over time, internal teams can take full ownership of the solution. 

A co-innovation approach also helps build trust as product, technical and external teams work together toward the same goal.

When to Build a Team In-House or Partner Externally (or Both)

Like many financial services firms exploring AI, you might be wondering whether to build in-house or outsource. 

In the early stages, when teams are small, overstretched, or lack AI expertise, it makes sense to partner externally. Working with an experienced external team helps you move quickly, validate ideas, and establish a solid foundation for future projects. An experienced AI partner can also help you recover momentum if you’ve tried internal projects that stalled or failed.

But as AI becomes strategically important to your products or operations, building in-house capability becomes essential. At that stage, owning your AI systems, data, and operational processes ensures adaptability, compliance, and scale as your business evolves.

This shift is similar to that of web development 25 years ago. Back then, financial companies often outsourced their websites because it was new and experimental. Today, no financial institution can operate without an in-house web team managing their online presence. 

That’s why it’s important to take a hybrid approach like a co-development model that focuses on empowering small AI teams to grow into experts and scale their own projects to avoid vendor lock-in. It allows you to start small, with or without an existing team, and begin developing AI projects like chatbots or intelligent document processing quickly. You can then gradually work your way up until you have an expert team that can independently ideate, test and deploy AI-powered solutions based on your business needs. 

This is the approach we take at Neurons Lab.

How To Build An AI Development Team With Neurons Lab

As an AI consultancy and enablement firm, Neurons Lab helps you get started with co-innovation. You’ll access the technical expertise and structure needed to build and deploy early AI systems while also transferring knowledge to develop your teams’ skills and set up proven processes. This approach builds your internal capability in key areas such as AI operations, quality metrics, and product management, ensuring a smooth transition to full in-house ownership over time. 

Specifically, you’ll receive:

• Education and technical enablement services to train your existing team or help you understand the roles needed, along with the types of profiles, qualifications and experience to look for so you can find the right talent and build an AI team.
• Engineering and infrastructure services to help you build, deploy, and manage AI systems effectively
• Strategic alignment and leadership workshops to set expectations, define metrics, and guide ROI thinking. 

Here’s how you’ll build an able AI team for your AI projects with Neurons Lab: 

1. Get Project Delivery in 2-4 months with our 500+ AI Talent Network and Cross-Functional Teams at the Ready

When you need to get an AI project off the ground quickly, our global AI talent network provides access to established, cohesive cross-functional teams. With proven AI development processes, these teams already know how to work together. This allows them to be immediately assigned to your most urgent projects and reconfigured as needed, delivering AI solutions quickly, reliably, and at scale.

With ready-made teams, you can build, test, and scale your AI solutions quickly and go live in as little as two to four months.

2. Build AI skills through knowledge and skills transfer 

Through our education and technical enablement programs, we help you identify what kinds of roles, skills, and profiles you need to hire.

If you already have an engineering team, we provide hands-on training to upskill them in AI engineering and infrastructure, so they can build frameworks and scale into more advanced use cases on their own.

If you don’t already have a team, we can help you identify existing staff with relevant skills (e.g., cloud engineers, data engineers, data scientists) to help form your new AI team and deliver your first PoC with current resources.

For non-technical teams, we offer practical training on how to use AI-assisted tools effectively in daily workflows to improve productivity. For example, we supported a major bank in Ukraine through a GenAI enablement training program designed to empower its marketing team with:

• A clear understanding of GenAI’s capabilities, limitations, and practical applications for process and task optimization.
• Hands-on training on how to use AI tools effectively.

After the program, the bank’s team reported a higher creativity, productivity, and workflow efficiency. They also rated the training’s usefulness at 95% and an overall 91% satisfaction. Read the full case study here

Neurons Lab’s Gen AI educational program covers selecting AI tools, key skills and an AI roadmap

3. Increase Team Collaboration through Co-Development

To help manage tension between internal and external teams, we position ourselves as allies, not replacements. 

Our aim is to strengthen capability and ownership to prevent vendor dependency. So we work alongside your teams to share knowledge, build confidence, and ensure they feel supported. We do this through role-specific training, post-launch workshops, and ongoing enablement programs to ensure AI adoption is sustainable and embedded in your organization’s DNA.

And by proving success together, we help them secure larger budgets, and gain stronger backing for future AI initiatives. 

For example, a leading global payments provider was struggling with slow, inconsistent content localization across regions. To close this marketing efficiency gap, we co-developed an LLM-powered content constructor while empowering and training existing teams to take over the project afterward.

Example of tone and channel selection in the LLM-powered content constructor. The system adapts responses based on user inputs to deliver consistent, localized messaging. Image Source: Neurons Lab

The solution now supports nine of the provider’s high-priority markets, enabling marketing teams to create localized and compliant marketing messages across 20 languages and eight marketing channels. 

We deployed the solution on AWS cloud architecture, designing it specifically for the financial sector. The model incorporates regulatory and data privacy requirements to guarantee accuracy, security, and full compliance across global operations. As a result, marketing teams reduced content creation time and improved localization accuracy at scale. 

4. Set up Governance and AI/ML Ops for Better Control and Performance

Poor governance makes it difficult to measure progress, resolve issues, or improve AI systems. 

Neurons Lab helps you establish the governance and AI operations frameworks that give your team full control and transparency. 

We put in place the AIOps and MLOps structures, processes, metrics, and tools needed to monitor performance, trace model decisions, and manage AI systems more effectively. With this foundation, your teams can diagnose issues accurately and continuously enhance reliability and outcomes.

5. Align your Leadership on AI Strategy, Expectations, and Metrics

Many leaders measure AI success using traditional digital benchmarks, which don’t reflect its complexity, broader impact, or long-term value. We help align your leadership on the right expectations and ROI mindset for AI through executive workshops. 

These sessions clarify goals and set realistic success metrics. They also help leadership understand that AI creates value not only through efficiency but also by enabling automation, new business models, and greater accessibility.

How A Leading Asian Bank Grew RM Client Assets by 30% with Neurons Lab

A major Asian bank wanted to grow its wealth management division but was limited by the strain on its relationship managers. The bank had already started exploring AI as a way to achieve this and had identified three key goals:

• Increase relationship capacity to handle more clients with less effort
• Grow assets under management per manager
• Improve client service quality, measured by Net Promoter Score (NPS)

To understand the root cause of the capacity strain, we worked closely with the bank’s relationship management teams, mapping their workflows to identify repetitive, time-consuming tasks that limited client engagement.

Our analysis found that asset managers managed 200 to 250 clients each, yet spent much of their time on manual work, including:

• Nine hours a week gathering market insights
• Two hours a day preparing client messaging
• Three hours building a single client deck
• Three hours a week on opportunity triage

This lost time, spent on preparation and compliance, caused managers to miss opportunities, struggle with consistent client engagement, and ultimately fall short of their KPIs. 

In response to these findings, we co-developed a tailored AI co-pilot. Through hands-on collaboration, we also transferred the knowledge and skills needed to maintain and scale the solution across departments. With access to instant market insights and automation for routine tasks, deployment is currently underway with the goal of delivering:

• 30% more client capacity per manager, without adding headcount
• A higher level of consistent client engagement
• Improved NPS scores in the range of 15 to 20%

Why Choose Neurons Lab To Build Custom AI Teams and Solutions for Financial Services

As an AWS Advanced Tier Partner with AWS competencies in Generative AI and Financial Services, we have a proven track record of delivering custom AI solutions for leading financial firms like Visa, AXA, and Oschadbank. By choosing us, you gain an end-to-end AI partner that helps you build internal AI capability through structured training, programs, and governance frameworks.

Our deep financial services expertise means we understand complex FSI workflows, and have experience across use cases like fraud detection, AML/KYC automation, credit risk modeling and regulatory reporting. This directly informs our co-development approach, enabling you to build custom AI solutions that meet regulatory and compliance standards while aligning with your goals, processes and existing systems. 

Once your solutions are live, you’ll have our ongoing support to monitor performance, gather insights, and make continuous improvements. Your teams will also receive post-launch enablement and knowledge transfer, empowering them to manage and scale these systems independently over time.

Work with an AI Development Partner to Build Lasting Capability

Building your own AI team can take months and still doesn’t guarantee success in proof of concept (PoC) or deploying custom AI solutions. Bringing in a temporary external team may help you realize value faster, but it may not build long-term capability or prepare you for a shifting AI landscape.

An AI development partner invested in co-creation helps you build a proven team from the ground-up (if needed), work alongside your existing teams to deploy compliant, custom AI solutions, and transfer knowledge for independent sustainability. 

At Neurons Lab, we deliver this kind of long-term value through our co-development approach. 

If you’re ready to co-develop tailored AI solutions while empowering your teams and building internal AI capability, book a call with us today.

FAQs on Building AI Development Teams for Financial Services Firms

How do AI governance and MLOps improve project success in financial services?

By ensuring compliance, transparency, and reliability, AI governance and MLOps help you deploy AI systems that are compliant, consistent, and ready for complex financial services workflows. Governance establishes clear standards for fairness, security, and accountability, while MLOps provides the structure to monitor, retrain, and scale models efficiently. 

What’s the ROI of building an AI team for financial services?

Building an AI team delivers ROI by giving financial institutions in-house expertise to develop, deploy, and manage AI solutions. It reduces reliance on external vendors, speeds up innovation, and ensures the organization can adapt to new AI advancements. This capability helps firms stay competitive, improve efficiency, and create new revenue opportunities across products and services.

Should we build an AI team in-house or partner with an external AI development company?

It depends on your short- and long-term goals. If you need to deploy AI quickly, partnering with an external expert makes sense. If you see AI as a long-term strategic capability, building an in-house team is the best approach. However, working with a specialist consulting firm like Neurons Lab can offer you both through a co-development model. You get fast deployment backed by experienced teams combined with knowledge transfer and training to help build in-house AI expertise.

What’s the best way to structure an AI development team for financial services?

The best structure for an AI development team in financial services combines technical, product, and governance roles. Core members include an AI or machine learning engineer, software or cloud engineer, and infrastructure lead, guided by a technical architect. A project or product manager ensures delivery and compliance, while leadership oversees metrics, alignment, and integration with business goals.