5 AI Roles You Need for Your Next-Gen Product

The landscape of AI roles is rapidly changing. Recent reports from industry sources indicate that there has been a 323% rise in demand across the AI talent pool, with GenAI roles leading the pack. Companies from all industries are spending a range of $200K to $2M per year just to hire AI talent as they start to deploy intelligent capabilities in their products. AI has changed the game in innovation/product development across every sector—from FinTech to HealthTech.

Twenty-first-century AI product development requires specialized talent. Generic software engineering teams cannot effectively manage the data and complexity of machine-learning pipelines, large language models, and data-driven decision systems. The need for specialized AI roles has increased due to the complexity of AI systems. Each AI role has its own unique skill set that traditional developers do not possess.

The adoption of GenAI has been explosive. Over 65% of medium-sized technology companies currently use generative AI in production, in whole or in part. As a result, there is a great demand for Generative AI roles such as prompt engineers or large language model specialists. Companies are seeking professionals who understand transformer architectures, fine-tuning, and prompt optimization.

AI is beyond merely a feature; it's a foundation. AI development teams are now building entire ecosystems around intelligent automation. From customer service bots to predictive analytics dashboards, AI permeates every product layer. This requires them to have a variety of roles in the AI teams working together in sync.

Companies without AI will soon fall behind. Fast-growing startups (50-500 employees) know the right set of AI roles and responsibilities is the difference between success and failure in the market. CTOs with a product development champion know (or are starting to realize) that they need to assemble AI development teams that are capable of delivering next-gen features in a timely fashion.

Core responsibilities and skills

Essentially, AI engineers build the architecture of intelligent products. Their work involves designing the AI systems, integrating the API's, deploying models, and maintaining production pipelines. AI engineers typically leverage programming and tooling like Python, TensorFlow, PyTorch, cloud services (AWS, GCP, Azure), and MLOps tools.

AI engineer roles focus on:

• Architecting and building scalable AI solutions
• Implementing an architecture for model serving
• Optimizing the speed and cost of inference
• Building data pipelines for training AI models
• Ensuring the security of AI systems

Without AI engineers, your AI development team suffers from a lack of technical leadership. AI engineers are needed to bridge the gap from research-type models to enterprise-grade production systems. The average salary for AI engineers ranges from $130K - $180K. The demand in 2024 for AI engineer roles increased by 178%.

Machine learning engineers are responsible for developing algorithms to learn from data. They create recommendation engines, fraud detection models, and predictive maintenance applications. Their work involves feature engineering, model selection, hyperparameter tuning, and A/B testing.

Key responsibilities include:

• Training custom ML models
• Evaluating metrics for model performance
• Applying ensemble methods
• Testing experiments and backtesting
• Monitoring models and ensuring performance over time

Machine learning engineers make products smarter. They enable personalization, automation, and intelligent decision-making. GenAI roles tend to overlap in this area since users are also working with foundation models. Compensation ranges: $140K-$200, and job postings were up 156% year-over-year.

Data scientists are the strategists of the AI-development team. They mine data for patterns, create statistical models, and communicate what they have found to stakeholders. They work with structured and unstructured data to solve business problems.

Key AI roles and responsibilities include:

• Exploratory data analysis
• Hypothesis testing and validation
• Building classification and regression models
• Data visualization
• Collaborating with product managers

Data scientists identify opportunities for the AI development of products. They discover what problems AI can and cannot solve. They determine which issues AI can address and which can’t. They provide the analytical framework for every job in the AI team. Pay ranges from $120K and $170K. Demand is still very high in all industries.

Prompt engineers are among the very newest GenAI roles. They create input that results in quality relevant outputs in large language models. This role emerged as companies started adopting GPT, Claude, and other models like it.

Responsibilities include:

• Writing and testing prompts
• Fine-tuning language models
• Building RAG (Retrieval-Augmented Generation)
• Optimizing tokens and costs
• Ensuring responses are of quality and safe

NLP experts are the people who enable AI to understand human language. They work on sentiment analysis, branding recognition, and conversation design. Generative AI-focused roles, such as prompt engineers, have become indispensable to chatbots,virtual assistants, and tools for generating content. Pay ranges from $100K to $150K. Demand has risen 412% since early 2023.

AI product managers convert business ambitions into technical specifications. They possess a deep understanding of the limitations and capabilities of the AI solution they are delivering. They prioritize features, manage the backlog, and act as a liaison between stakeholders and technical teams.

Core Functions:

• Define the AI product roadmap
• Conduct market research
• Determine criteria for success
• Lead cross-functional teams
• Evaluate AI tools and vendors

This AI role guarantees that your AI development team spends its time building useful products for users. The role requires balancing innovation and AI limitations vs. feasibility. They know the various AI team roles and lead them to collaboration as a group. The average compensation is between $130K - $190, and in 2024, hiring in this area increased by 203%.

A successful AI product is the result of careful strategy in team design. Start your team with core AI roles: one AI engineer, one data scientist, and one ML engineer. As you scale your organization, include GenAI roles. At the level of CTO, AI roles and responsibilities should be mapped to specific product features.

Collaboration framework:

• Data scientists surface possibilities
• ML engineers build models
• AI engineers deploy systems
• Prompt engineers optimize user-facing interaction with LLM
• An AI product manager coordinates delivery

Cross-functional integration is vital for your AI development team to collaborate with your established product, design, and engineering teams. AI teams that are siloed fail. When cross-functional teams collaborate on AI, you dramatically speed up the development of AI products. Use shared documentation, regular check-ins, and a common set of roadmaps.

AI is expanding fast. Opportunity continues to arise. Employees should have a sense of ongoing learning. Some of the most widely held certifications and courses are AWS Machine Learning Specialty, Google Cloud Professional ML Engineer, and TensorFlow Developer Certificate. Each individual in an AI development team should ideally complete 2-3 courses each year.

For employees entering roles at an AI team, a portfolio matters much more than a degree. Their portfolio should document deployed models, GitHub repositories, and participation in Kaggle competitions. Documentation of business impact: "Decreased churn by 23%" will certainly have more impact than "built classification model".

Attend AI conferences, become engaged in open-source projects, or participate in research sub-communities. Connecting with people and the community can help you build better hires and knowledge sharing. Looking for upward trends with GenAI for an AI mindset is also valuable. You can find trends on sites like Hugging Face, Papers with code, and via various AI labs.

Selecting the correct AI roles will speed up your innovation and competitive advantage. As companies move from experimentation with AI to enacting it as a part of business operations, building a capable, nimble AI development team is no longer discretionary.

Companies that invest in specialized AI professionals, ranging from an AI engineer to a prompt engineer, will control their respective markets. Whether you are a scaling CTO, founder with a vision, or talent strategist, the time to invest in these five AI roles is now. Just stop and consider your needs and where to hire for those needs, and it will fuel your next-generation product.