AI Engineers Must Specialize in 2026
Realizing you don’t know everything is the gateway to a greater future
“How do you keep up with all of it?” I stared at the LinkedIn message with the loaded question. It was a common query I got probably once a week. “What’s your secret?” “How do you stay on top of all the updates?” I usually gave a canned answer: “oh, I spend a lot of time reading updates and diving into new frameworks.” But something in me shifted, and I typed out a new answer: “I don’t.”
My reason for this new answer is that I’ve come to recognize an evolution in the applied AI industry that has been taking place over the last five years. Perhaps early on it was possible to know everything about leveraging LLMs in a business setting, when the only model that mattered was GPT-3.5 Turbo and RAG was the hot new trend. But today, I find myself painfully aware of the gaps in my knowledge as I look across the vast landscape of generative AI that started from those early seeds. I’ve also come to accept those gaps and take joy in my specialties.
“I don’t know everything about AI” is maybe a risky thing to share with the world, considering I’m trying to persuade people to buy a book about AI from the position of “I am a person that knows a lot about AI”. I certainly spend a lot of time thinking about AI. How it affects my organization. Why it’s powerful. What I can do to help people learn how to use it. But I also have shallow knowledge on areas like building MCP servers or designing advanced knowledge management systems. The truth is, the reach of generative AI has grown so broad that it is impossible for a single person to execute everything you can do with it deeply and effectively in all areas. AI engineers must learn to specialize. I’ve decided that I’m comfortable with the areas I am specialized in, and can accept my shortcomings in the other areas.
This gap between the desire to know everything and the reality of needing to specialize can cause problems if you don’t learn to accept it. You’ll waste time, constantly churning and attempting to consume everything, when in reality you could be diving deeper in any sub-area. Become an expert in knowledge management systems. Establish yourself as a wizard with evals. Build an MCP server or AI friendly CLI. Learn how to deploy autonomous agents. Go deep with local models, or focus on connecting to AI via paid APIs. But it’s dangerous to be a generic jack-of-all-trades. My friend Carlos Kenemy said something wise:
It is no longer enough to be broad OR deep. You must do BOTH to succeed in the age of AI.
Carlos Kenemy
In the world of recruiting, this is known as “T-shaped”. You search for talent that has both a broad (yet admittedly shallow) understanding of many things, while also excelling in a single area. I feel like at Pattern, we’ve been fortunate in finding many new team members that fit that description.
The key to being a successful T-shaped AI engineer and finding where to go deep is humility. Humility is what allows you to recognize gaps in your knowledge. Having gaps is ok. But you need to know which gaps need filling, and which gaps can be strategically neglected. I could go get a GCP certification for AI. But, perhaps my time would be better spent attaining an AWS certification since I don’t use GCP at my company. Time is short. I can’t do everything. Instead, go deep where you have talent, have passion, and there is need in your current company or in the market.
Here are some potential areas an AI engineer could focus in 2026, many of which have many openings and few people specializing in them:
Evaluations: Understanding how to build benchmarks, define metrics, run A/B tests, and optimize agents is a powerful skill that not many have. Tools like Langfuse and Opik are quickly becoming a requirement on job descriptions.
Tools: Tools are the key to AI agents that deliver read value. Plugging in an MCP server you found on the internet is easy, but building MCP servers and handling them at an enterprise scale is much more challenging. How do you handle RBAC, discovery, and definitions? Tools like FastMCP are key.
Running open-source models on infra: This is a powerful skillset with a wide range of expertise. On one end of the spectrum are lots of new entrants jumping into the field, fulfilling the massive demand from companies that demand on-prem inference for privacy reasons or customization to domain problems. On the other end of the spectrum are people with PhDs in NLP, who have deep technical skills to actually undercut hyperscalers and outperform out-of-the-box models with advanced customization techniques.
Workflows (no-code): Building AI workflows that can be repeatedly and deterministically run is a powerful technique, often superior to open-ended AI agents. Tools like Zapier, n8n, OpenAI Agents, and more offer non-coders a mirage of the ‘easy’ solution that vanishes once reality hits. Operating these tools at scale is challenging. Having an expert to lead an organization in building these workflows is invaluable.
Agents: AI running in the cloud, using tool harnesses, solving problems. It’s the holy grail. This speciality sits at the intersection of many different specialities, and can often shapeshift depending on how a company is using the term ‘agent’. Tools like Temporal, CrewAI, LangGraph, AgentCore, AI SDK, and more are beneficial for these specialists.
AI SDLC: Dario Amodei said 90% of coding would be AI generated by the end of 2025. He was right. In a follow interview, he said the next step is reaching 100% (much harder) and attacking the steps before and after coding: problem definition and solution validation. That transformation is happening as you read this. People with expertise in AI-driven Software Development Life Cycle (AI-SDLC) can revolutionize how companies create software, raising the productivity of entire engineering departments.
Knowledge Management: The once humble Retrieval Augmented Generation (RAG) technique has evolved into a highly complex discipline known as ‘knowledge management’. This is much more than just vector databases. You’re dealing with the scale of big data and the complexity of unstructured data, trying to handle RBAC for sensitive documents while also maximizing retrieval accuracy and relevancy at the lowest cost point possible. If you feel like you can tackle all of that, then you’re ready to specialize in knowledge management.
Training and consulting: They say those who can’t do, teach. The reality is that being an expert in how generative AI works and knowing how to train a room of 100 people to use ChatGPT effectively are not the same thing. People who can effectively teach and lead people in AI are sorely needed, and those teachers are typically high skilled experts who find joy in sharing knowledge.
Observability: Congratulations, you’re using AI! But who’s using it? What’s going to the model? How much does it cost? Questions like these can go unanswered if companies have no cohesive strategy when it comes to AI access. Having a strategy for AI observability is critical. Understanding OpenTelemetry and enterprise resource management are key in this area.
This is not an exhaustive list, and I’d love to hear what other areas of specialization you are noticing. Hopefully, for those searching right now in college and in the middle of their careers, this is helpful. To circle back to the book I referenced (Architected Intelligence), this diversity of specializations is displayed throughout the different chapters of the book and can be a helpful guide.
The bottom line: AI engineering is hot right now, but you’ll be even hotter if you learn to specialize inside of that nebulous title.