When people talk about building an AI company, the conversation almost immediately gravitates toward the “hard” technical roles: the machine learning engineers, the research scientists, the backend architects. It’s a natural bias; we tend to view AI through the lens of code and math because those are the tangible levers of capability. But anyone who has actually deployed a model into the messy, unpredictable chaos of the real world knows a different truth. The code is often the easy part. The real challenge lies in the gap between a mathematical abstraction and a functioning, trustworthy system.
If you are hiring for an AI startup and your org chart looks like a standard software engineering team with a few PhDs sprinkled in, you are setting yourself up for a specific kind of failure. It’s the failure of the “brittle demo”—systems that work perfectly in a controlled notebook environment but crumble the moment a user inputs something unexpected, or when the cost of running the model eats into your margins, or when a regulator asks how you ensure fairness. To bridge that gap, you need roles that don’t exist in traditional software development. You need specialists who speak the language of data, not just algorithms.
The Knowledge Engineer: The Architect of Context
Let’s start with a role that sounds archaic but is arguably the most critical for the current generation of Large Language Models (LLMs): the Knowledge Engineer. In the early days of AI, this role belonged to the era of expert systems and symbolic logic. Today, it has been reborn as the essential bridge between raw model capability and domain-specific utility.
Most engineers are trained to solve problems by writing logic. If X happens, do Y. But modern AI systems, particularly retrieval-augmented generation (RAG) pipelines, require a different mindset. They require the curation of knowledge. A Knowledge Engineer doesn’t just feed a model data; they structure it.
Consider a healthcare AI startup. You have a massive corpus of medical journals, patient records, and clinical guidelines. A standard software engineer might dump this into a vector database and call it a day. The Knowledge Engineer, however, looks at the relationships between entities. They understand that “Aspirin” is not just a token; it’s a chemical compound that interacts with “Warfarin,” treats “Headache,” and has contraindications with “Pregnancy.”
Their job is to design the ontologies and knowledge graphs that allow the AI to reason, not just retrieve. They determine how to chunk text, how to embed relationships, and how to prompt the model to utilize this structured context effectively. This is not data engineering, which focuses on the plumbing of data movement. It is not data science, which focuses on statistical analysis. It is the art of encoding human expertise into a form that a probabilistic model can consume without hallucinating.
Without a Knowledge Engineer, you end up with a chatbot that confidently cites non-existent laws or a financial advisor that misses obvious regulatory constraints. They ensure the “grounding” of the model is solid. In an industry obsessed with parameter counts, the Knowledge Engineer is obsessed with the quality and structure of the information flowing through those parameters.
Why Traditional Data Engineers Fall Short
It is worth pausing here to distinguish this from the data engineering role that every startup needs. Data engineers build pipelines; they ensure data moves from Point A to Point B efficiently and reliably. They are concerned with throughput, latency, and storage formats. A Knowledge Engineer is concerned with semantics.
Imagine you are building a legal AI. A data engineer ensures the PDFs of case law are stored in S3 and indexed. A Knowledge Engineer analyzes the citation network within those PDFs, identifies the hierarchy of legal precedent, and structures the data so that when a user asks a question about contract law, the model retrieves the relevant statutes and the conflicting case rulings that provide nuance.
This distinction becomes vital when dealing with “unstructured” data. In reality, data is never truly unstructured; it just lacks a schema imposed by a human. The Knowledge Engineer imposes that schema, often dynamically. They are the ones who decide that a “customer” entity in your vector store needs to be linked to “support tickets,” “purchase history,” and “sentiment analysis” to provide a holistic view to the LLM.
Startups that skip this role often find themselves in a cycle of “prompt engineering hell,” trying to coax reliable answers out of a model that simply doesn’t have the right context window or retrieval strategy. The Knowledge Engineer solves this upstream, by making sure the model is fed the right information in the right format from the start.
The Evaluator: The Guardian of Quality
Once a model is trained or fine-tuned, and the knowledge base is structured, you enter the deployment phase. This is where the Evaluator comes in. This is not the same as a Quality Assurance (QA) engineer in traditional software. A QA engineer looks for bugs—code that executes incorrectly. An Evaluator looks for degradation in a probabilistic system.
Traditional software is deterministic. If you write a function to sort a list, it should sort the list the same way every time. AI models are stochastic. They generate different outputs for the same input based on temperature settings, sampling methods, and the inherent randomness of the neural network. Evaluating this requires a completely different toolkit.
The Evaluator is part statistician, part domain expert, and part adversarial tester. Their primary output isn’t code; it’s metrics. But these aren’t just accuracy scores. In production, accuracy is often a vanity metric. An Evaluator cares about “faithfulness” (does the answer stick to the provided context?), “hallucination rates” (is the model making things up?), and “toxicity” (is the output harmful?).
One of the most challenging aspects of this role is designing the evaluation framework. You cannot simply rely on human review at scale; it’s too slow and expensive. You cannot rely entirely on automated metrics like BLEU or ROUGE, because they often correlate poorly with human judgment. The Evaluator builds a hybrid system.
They might use “LLM-as-a-judge” techniques, where a powerful model like GPT-4 evaluates the outputs of a smaller, specialized model. However, the Evaluator knows the limitations of this approach—bias in the judge model, circular reasoning—and designs guardrails to correct for them.
Consider a startup deploying a coding assistant. An Evaluator doesn’t just check if the code runs. They check if the code is idiomatic, if it follows the company’s style guide, if it imports the correct libraries, and if it handles edge cases gracefully. They create “golden datasets”—curated sets of inputs with known high-quality outputs—to regression test the model every time a parameter is tweaked.
Without this role, teams fall into the trap of “vibe checking” their models. They look at a few outputs, nod approvingly, and ship. Three weeks later, user complaints flood in about the model degrading because a subtle change in the embedding model shifted the retrieval distribution. The Evaluator catches these drifts before they become churn.
The Nuance of Adversarial Evaluation
What makes a great Evaluator is their ability to think like a malicious user. This is where the role overlaps with security but remains distinct. In traditional cybersecurity, you scan for SQL injection or buffer overflows. In AI security, you are looking for prompt injection, jailbreaks, and data leakage.
The Evaluator spends their days trying to trick the system. They craft inputs designed to override system instructions. They probe the model for bias, testing if it treats demographic groups differently. This requires a deep understanding of how models “think.”
For example, an Evaluator at a real estate AI startup might test if the model recommends neighborhoods based on biased historical data. They don’t just look at the output; they analyze the latent features of the embeddings to see if concepts like “wealth” are dangerously correlated with protected attributes.
This role is often undervalued because its success is invisible. When an Evaluator does their job well, nothing happens. The model works, users are happy, and no lawsuits are filed. But the risk of skipping this role is catastrophic. A single viral incident of a chatbot giving dangerous advice can destroy a company’s reputation overnight. The Evaluator is the insurance policy against that reality.
The AI Auditor: The Arbiter of Trust
As AI systems move from novelty to infrastructure, the regulatory landscape is hardening. This brings us to the AI Auditor. This role is distinct from the internal Evaluator. While the Evaluator focuses on performance and safety, the Auditor focuses on compliance, governance, and explainability.
The AI Auditor is a hybrid of a legal scholar, a risk management consultant, and a technical investigator. They look at the entire lifecycle of the model—from data acquisition to deployment—and ensure it meets external standards (like the EU AI Act, HIPAA, or GDPR) and internal governance policies.
One of the primary tasks of an AI Auditor is documentation. In software, we often rely on code comments and READMEs. In AI, that is insufficient. An Auditor requires “Model Cards” and “Datasheets for Datasets.” They need to know exactly what data the model was trained on, who labeled it, and what the license of that data is.
This is becoming a massive legal issue. If a startup trains a model on copyrighted code or text, the Auditor is the one who assesses the infringement risk. They trace the lineage of data. They ensure that user data used for fine-tuning was collected with proper consent.
Beyond data, the Auditor looks at decision logic. If your AI denies a loan application or screens a resume, you need to be able to explain why. “The model said so” is no longer a legally defensible position in many jurisdictions. The Auditor works with engineers to implement interpretability tools. They might demand SHAP values (Shapley Additive Explanations) for every prediction, ensuring that every output can be traced back to specific input features.
This role is particularly crucial for B2B AI startups selling to enterprise clients. Enterprise buyers are risk-averse. They will ask for your audit reports before they sign a contract. They want to know that your model is robust, fair, and secure. The AI Auditor prepares those reports. They speak the language of compliance officers and CISOs, translating technical jargon into risk assessments.
The Intersection of Ethics and Engineering
There is a misconception that ethics in AI is purely philosophical. The AI Auditor proves otherwise. They operationalize ethics. They take abstract principles like “fairness” and “transparency” and turn them into testable criteria.
For instance, if a company commits to “non-discrimination,” the Auditor defines what that means mathematically. Does it mean equal opportunity (equal true positive rates across groups)? Or demographic parity (equal selection rates)? The Auditor decides which metric applies to the specific use case and ensures the engineering team monitors it.
This role also manages the “human-in-the-loop” protocols. Many high-stakes AI systems require human oversight. The Auditor designs the workflows for that oversight. Who reviews the AI’s decisions? How are disputes handled? What is the escalation path? They ensure the system isn’t just technically sound but operationally humane.
In many ways, the AI Auditor is the conscience of the company, but with the technical teeth to enforce their will. They don’t just suggest guidelines; they implement checks in the CI/CD pipeline that prevent non-compliant models from being deployed. They are the gatekeepers ensuring that the pursuit of innovation doesn’t outpace the responsibility of deployment.
The Human-in-the-Loop Specialist: The Trainer
While not always a C-suite level role, the Human-in-the-Loop (HITL) Specialist is a vital operational position that is often overlooked. As models become more capable, the need for high-quality training data doesn’t disappear; it shifts. We move from collecting massive datasets to collecting highly specific, difficult edge cases.
The HITL Specialist manages the interface between human experts and the AI model. They are responsible for “Reinforcement Learning from Human Feedback” (RLHF) pipelines. This is not just data entry. It requires an understanding of the model’s current weaknesses.
A HITL Specialist looks at the model’s failures and curates a dataset of examples specifically designed to correct those failures. They work with subject matter experts—doctors, lawyers, coders—to generate high-quality preference rankings. They know how to write instructions that help human labelers understand what a “good” output looks like.
Without this role, fine-tuning often becomes a waste of compute. You end up training on noisy, inconsistent data that confuses the model rather than refining it. The HITL Specialist ensures that every dollar spent on compute translates into measurable performance gains by maximizing the signal-to-noise ratio in the training data.
The Model Reliability Engineer (MRE)
Finally, we need to talk about the infrastructure that keeps models running. This is the realm of the Model Reliability Engineer. While a standard DevOps engineer handles server uptime, an MRE handles model uptime.
Models are resource-hungry and fragile. They suffer from “concept drift” where the statistical properties of the target variable change over time. A model that predicts stock prices well in a bull market may fail miserably in a bear market. The MRE monitors these shifts.
They implement the observability stack specifically for AI. Standard logging tells you if a server is down; AI observability tells you if the distribution of input data has shifted. The MRE sets up alerts for when the average embedding vector of incoming requests drifts significantly from the training set.
They also manage the complexity of inference. As a startup grows, you might need to serve models on different hardware (GPUs vs. CPUs), manage batch processing vs. real-time requests, and optimize for cost. The MRE decides when to quantize a model (reducing precision to save memory) and when to distill a large model into a smaller one. They ensure that the latency of the AI doesn’t destroy the user experience.
Building the Team: A Synergistic Approach
When you look at these roles together—Knowledge Engineer, Evaluator, Auditor, HITL Specialist, and MRE—you see a pattern. They all exist to solve the friction between the idealized world of machine learning research and the gritty reality of production software.
Hiring only engineers and scientists creates a blind spot. Engineers are trained to build; scientists are trained to discover. But AI in production requires stewardship. It requires curation, validation, and governance.
If you are a founder or a hiring manager, the temptation is to find “unicorns”—people who can do all of this. Don’t. The disciplines are too deep. It is better to hire a Knowledge Engineer who loves ontologies and an Evaluator who loves breaking things than to hire a generalist who does neither well.
Furthermore, these roles foster a culture of rigor. When an Auditor asks for the datasheet of a training set, it forces the data team to clean up their act. When an Evaluator builds a regression suite, it forces the scientists to care about production metrics, not just validation loss. This cross-pollination creates a resilient organization.
As AI continues to permeate every industry, the competitive advantage will not just be who has the smartest algorithm. It will be who has the most robust system. The companies that win will be those that invest in the unglamorous, essential roles that ensure the technology serves the user reliably, ethically, and sustainably. They are the silent architects of the AI future.
