AI systems engineer: I treat the model as one component of the larger software system that carries it

The seven areas of the discipline

I treat AI as a component of a larger software system, not a standalone feature; the seven areas are one whole, from designing that component to operating it.

• AI Systems Architecture

  Architecture is the backbone of AI Systems Engineering. In AI-native and AI-enabled systems I separate the model, application and infrastructure layers and keep components replaceable. In a provider-agnostic architecture, changing the model should leave the application untouched; I build that with a layer such as an AI Gateway, with fault tolerance and scalability in mind.

• Model Engineering & Customization

  I own model selection and evaluation, and domain-specific adaptation. Fine-tuning and efficient methods such as LoRA/PEFT, model training with TensorFlow and PyTorch, and Machine Learning Research sit inside this area. I reduce latency and cost with Inference Optimization and put the model behind an API with Model Serving; small, local and open-weight models are among my options.

• Agentic AI & Orchestration

  I treat AI Agents as unreliable decision-makers and run them inside control systems: the model proposes, code decides. In Tool Calling I design the tool contract, permissions and state; in Human-in-the-Loop I decide where human approval sits. Protocols such as the Model Context Protocol and the Agent2Agent Protocol, and orchestration and authority boundaries in Multi-Agent Systems, belong to this area too.

• AI Integration & Data Pipelines

  In Retrieval-Augmented Generation the whole pipeline is mine, from parsing a document and drawing chunk boundaries to producing Embeddings and fusing Vector Search with full-text search. I build third-party and enterprise integrations, ETL/ELT flows, event-driven workflows and queues as parts of the data pipeline around the model.

• AI Infrastructure & Platform Engineering

  I build AI Gateways, model routing and multi-provider integration, self-hosted systems, containerized deployment and AI delivery pipelines. Resource management for inference workloads, Model Serving on GPUs and Kubernetes, and infrastructure automation are the everyday work of this area.

• Reliability, Security & Observability

  An unreliable AI system is a risk, not a feature. I design guardrails and policy enforcement, Prompt Injection and tool execution security, data privacy and access control. I manage tokens, latency and cost as a budget; I watch requests with LLM Observability, catch regressions with LLM Evaluation, and contain failure with retry, timeout, fallback and circuit breakers.

• Applied AI Engineering

  I apply AI to real problems: extending an existing software product with AI capabilities, AI-assisted automation, developer tools and moving a research prototype into a production system belong to this area. The measure is not how impressive the model is but whether the problem is solved and the system stays operable.

Decisions I make in an AI feature

Before which model gets chosen, these four decisions around the model set how long the system lives.

01 Where the model sits

I put the model behind an interface instead of embedding it in the application. The cost is an abstraction layer and giving up provider-specific features; in return, changing the model or the provider is a configuration change, not a rewrite.

02 Budget

I set tokens, latency and cost when the request is designed, not when the invoice arrives. Because output tokens are only final once the response is complete, a budget limit cannot stop a single request midway; the overshoot is at most one request, and the one rejected is the next.

03 Authority

Every tool I give an agent is an authority. I define tools narrowly, tie irreversible actions to human approval, and treat any text that enters the model's context as input that can steer it.

04 Measurement

I do not judge output quality by looking once; I measure it with an evaluation set that can be rerun. When the model, the prompt or the retrieval changes, the regression should show up there first, not with the user.

The limit of the discipline

AI Systems Engineering is not only AI development, only ML engineering, only agentic AI or only AI infrastructure; it is the systems-focused engineering approach where they meet. That is why I do not tie the expertise to one technology, provider or trend. LLMs, RAG, MCP, agents and fine-tuning are today's technical components; when model architectures and protocols change, the questions of architecture, integration, security and operation stay where they are.