Artificial intelligence has transformed many roles and responsibilities in the technology world. Since around 2010, we have seen data-related roles such as Data Analyst, Data Engineer, and Data Scientist. However, today the landscape goes far beyond analyzing data to build machine learning models.
The widespread adoption of AI — and more importantly, how it is applied within software projects — has created entirely new job positions. These roles are built on strong programming foundations, but their daily activities are fundamentally different, centered around interacting with, orchestrating, and operationalizing artificial intelligence.
Below are some of the emerging roles shaped by this new AI-driven era.
Intelligence Engineer
(Applied AI Engineer)
The Intelligence Engineer designs and integrates artificial intelligence capabilities into systems, digital products, and development processes. Their goal is to turn AI models into practical, scalable solutions that meet business needs by automating tasks, making decisions, and creating measurable value.
Key activities include:
- Designing architectures that integrate AI models (LLMs, ML models, external APIs).
- Building pipelines using RAG, embeddings, and vector databases.
- Designing and optimizing structured prompts.
- Orchestrating agents and autonomous workflows.
- Evaluating and improving the quality of AI-generated outputs.
- Implementing validations, metrics, and security controls.
- Optimizing cost, latency, and overall model performance.
Agentic AI Expert
(Agent-Based AI Specialist)
The Agentic AI Expert specializes in designing, implementing, and optimizing systems built around autonomous AI agents. Their focus is creating architectures where multiple agents perceive, reason, make decisions, and execute actions in coordination to solve complex problems and automate end-to-end processes.
Key activities include:
- Designing autonomous agents and agent-based systems.
- Defining planning, memory, and decision-making mechanisms.
- Implementing agents with access to tools, APIs, and databases.
- Orchestrating communication and coordination between agents.
- Evaluating agent performance, stability, and alignment.
- Mitigating risks such as infinite loops, unintended actions, or goal drift.
- Optimizing cost, latency, and efficient model usage. Integrating agentic systems into business products and processes.
Agentic Systems Architect
(Architect of Intelligent Agent-Based Systems)
The Agentic Systems Architect is responsible for designing the architecture of systems powered by autonomous AI agents. This role defines how multiple agents perceive information, reason, make decisions, and execute actions in a coordinated way — ensuring scalability, security, and alignment with business objectives.
Key activities include:
- Designing scalable and resilient multi-agent architectures.
- Defining memory, planning, and decision-making structures.
- Designing coordination and communication mechanisms between agents.
- Integrating agents with APIs, databases, external tools, and enterprise systems.
- Establishing governance, control, and supervision rules for autonomous agents.
- Mitigating risks such as infinite loops, unintended actions, or strategic misalignment.
- Optimizing performance, latency, and model execution costs.
- Collaborating with product and business teams to translate strategic goals into functional autonomous systems.
AI Product Manager
The AI Product Manager defines, designs, and leads AI-powered products. This role connects AI technical capabilities with business and user needs, making strategic decisions about model usage, risk management, user experience, and technical feasibility to ensure AI delivers measurable value.
Key activities include:
- Defining the vision and strategy for AI-based products.
- Identifying high-impact AI use cases.
- Prioritizing features considering impact, cost, and risk. Collaborating with engineering and data teams on architecture and technical scope.
- Evaluating performance metrics (accuracy, latency, cost, adoption).
- Managing risks related to bias, privacy, and regulatory compliance.
- Designing user experiences that integrate AI clearly and responsibly.
- Continuously monitoring and optimizing product performance in production.
AI Tech Lead
The AI Tech Lead is the technical leader responsible for designing and overseeing the implementation of AI-based solutions within development teams. This role defines technical architecture, establishes best practices, and ensures AI systems are scalable, secure, and aligned with product and business goals.
Acting as a bridge between strategy, product, and engineering, the AI Tech Lead ensures technical excellence across AI initiatives.
Key activities include:
- Defining the technical architecture of AI-driven solutions.
- Leading the implementation of models, pipelines, and intelligent systems.
- Establishing quality, security, and governance standards in AI projects.
- Guiding teams on best practices for integrating LLMs, RAG systems, and agents.
- Reviewing code and validating AI-generated outputs.
- Making technical decisions regarding models, tools, and frameworks.
- Aligning technical decisions with product strategy and stakeholder objectives.
Agentic AI Engineer
The Agentic AI Engineer designs, develops, and implements autonomous AI agents. They build systems where agents can perceive, reason, plan, and act independently — integrating with tools, APIs, and real-world environments to automate complex processes.
Their focus is turning AI models into operational agents that function reliably within production systems.
Key activities include:
- Developing and implementing autonomous agents using LLMs and tools such as LangChain.
- Designing memory, planning, and decision-making mechanisms.
- Integrating agents with APIs, databases, and external tools.
- Building multi-agent architectures and coordination workflows.
- Implementing monitoring and behavior validation systems.
- Optimizing latency, cost, and execution efficiency.
- Testing, evaluating, and continuously improving agent performance.
- Here are a few examples of new roles created by the rapid adoption of AI in products and business processes.
If you know other roles created by this technological revolution, feel free to share them in the comments.
