# AI Engineer Published articles for AI Engineer. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Software Engineers Can Move Into AI Without Becoming Machine Learning Engineers DevFeed: [How Software Engineers Can Move Into AI Without Becoming Machine Learning Engineers]() Original publisher: [Read original article]() Author: Hemant Pandey Published: 2026-09-15T13:55:50Z Content type: opinion Language: en Sources: [The Hustling Engineer]() Topics: [Artificial Intelligence](), [Software](), [coding]() Tags: [ai](), [ai-engineer](), [software]() ### AI overview The article discusses how software engineers can move into AI without starting their careers over or becoming machine learning engineers. ### Source excerpt How software engineers can move into AI without starting their careers over ## What It Takes to Build a Production Agent Harness DevFeed: [What It Takes to Build a Production Agent Harness]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-14T19:50:37Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Agent Harness](), [LangChain](), [Artificial Intelligence](), [Persistence](), [Graphs]() Tags: [agent](), [ai](), [ai-engineer](), [engineering](), [evaluation](), [langchain](), [langgraph](), [memory](), [persistence](), [tools](), [tracing]() ### AI overview A hands-on series chapter explains how to build a production agent harness with LangChain and LangGraph. It covers model, message, prompt, and tool interactions; tool-call execution; state transitions; persistence; failure handling; tracing; evaluation; human approval; and resumable execution. ### Source excerpt A hands-on nanodegree for production agent engineering. ## Finding three-way API drift with the AI Engineer DevFeed: [Finding three-way API drift with the AI Engineer]() Original publisher: [Read original article]() Author: Talia Kohan Published: 2026-09-01T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [API](), [Postman](), [OpenAPI Specification](), [Claude](), [Tooling](), [client](), [servers]() Tags: [ai-engineer](), [api](), [api-drift](), [apis](), [ci](), [fintech](), [general](), [openapi](), [postman-ai-engineer](), [production](), [qa]() ### AI overview The article explains how to detect three-way API drift by comparing an OpenAPI specification, the requests in Postman Collections, and the responses returned by a running server. It argues that pairwise checks can miss cases where all three sources disagree, and describes using Postman AI Engineer and the Context Graph to identify these inconsistencies. ### Source excerpt Find three-way API drift with Postman AI Engineer and the Context Graph. Compares your OpenAPI spec, collection requests, and live server responses. The post Finding three-way API drift with the AI Engineer appeared first on Postman Blog. ## How AI-Generated Work Can Shift Complexity to Reviewers DevFeed: [How AI-Generated Work Can Shift Complexity to Reviewers]() Original publisher: [Read original article]() Author: Robin Wieruch Published: 2026-09-01T06:50:46Z Content type: opinion Language: en Sources: [Robin Wieruch]() Topics: [Artificial Intelligence](), [Pull Request](), [Localization (l10n)]() Tags: [agent](), [agentic-coding-team](), [ai](), [ai-code-review-burden](), [ai-engineer](), [ai-slop-pull-requests](), [env-file-security](), [review](), [workslop]() ### AI overview This commentary argues that AI-generated output can create additional work when it appears polished but leaves important decisions unresolved. Using pull requests as examples, it discusses number localization and how architectural choices can surface late in review. ### Source excerpt AI made producing work nearly free, so the job is absorbing complexity before handing work to someone else, with examples from tickets, PRs, and slide decks. ## 9 GPU Concepts Every AI Engineer Should Know DevFeed: [9 GPU Concepts Every AI Engineer Should Know]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-31T15:19:52Z Content type: tutorial Language: en Sources: [Dr. Ashish Bamania]() Topics: [GPU](), [PyTorch](), [Large Language Model](), [Artificial Intelligence]() Tags: [ai-engineer](), [artificial-intelligence](), [coding](), [compute](), [cpu](), [data-science](), [gpu](), [guide](), [internals](), [llms](), [machine-learning](), [nvidia](), [programming](), [pytorch](), [technology]() ### AI overview A practical guide to nine GPU concepts relevant to AI engineers. It introduces GPU computation hardware, memory, program execution, and multi-GPU connections, including streaming multiprocessors in NVIDIA GPUs and their role in parallel computation. ### Source excerpt A simple and practical guide to GPU internals that AI engineers actually need. Continue reading on Level Up Coding " ## GPU Concepts and Internals for AI Engineers DevFeed: [GPU Concepts and Internals for AI Engineers]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-27T12:16:32Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [GPU](), [Artificial Intelligence]() Tags: [ai-engineer](), [concepts](), [gpu](), [guide](), [internals]() ### AI overview A practical guide to GPU internals and concepts relevant to AI engineers. ### Source excerpt A simple and practical guide to GPU internals that AI engineers actually need. ## LLM inference batching strategies: static, dynamic, continuous, chunked prefill, and disaggregation DevFeed: [LLM inference batching strategies: static, dynamic, continuous, chunked prefill, and disaggregation]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-22T11:44:27Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [Large Language Model](), [AI Inference](), [Benchmark](), [GPU](), [Latency]() Tags: [ai-engineer](), [batching](), [gpu](), [inference](), [latency](), [llm]() ### AI overview A developer guide explains how static, dynamic, and continuous batching affect LLM inference throughput, latency, and GPU utilization. It also identifies chunked prefill and prefill-decode disaggregation as additional serving strategies. ### Source excerpt Static, Dynamic, and Continuous batching, Chunked prefill, and Prefill-Decode disaggregation, simply explained. ## Sentry's Greg Pstrucha on why a better prompt won't fix your agent's code DevFeed: [Sentry's Greg Pstrucha on why a better prompt won't fix your agent's code]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-06T00:04:02Z Content type: opinion Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Tooling](), [coding](), [Software](), [Large Language Model]() Tags: [agent](), [agents](), [ai](), [ai-engineer](), [code](), [evals](), [metrics](), [quality](), [sentry](), [software](), [software-engineer](), [test](), [tests](), [tooling]() ### AI overview Sentry staff engineer Greg Pstrucha argues that improving AI-generated code requires more than better prompts. In his discussion of the "Stop Prompting" talk, he recommends linters, strong tests, type systems, frameworks, and skills as baseline safeguards, supplemented by evaluations for harder-to-codify quality judgments. ### Source excerpt Sentry staff engineer Greg Pstrucha on linters, stronger tests, evals for Seer, and the quality metrics agents game -- from AI Engineer World's Fair 2026. ## Zed's Anant Goel on evals, agent context, and the limits of git DevFeed: [Zed's Anant Goel on evals, agent context, and the limits of git]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-05T23:21:14Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Development](), [Code](), [Git](), [datasets]() Tags: [2026](), [agent](), [agents](), [ai](), [ai-agents](), [ai-engineer](), [code](), [developers](), [evals](), [git](), [models]() ### AI overview Zed AI engineer Anant Goel discusses evaluating every new model release, avoiding vanity metrics, and maintaining a stable code-editor experience as users adopt different models and providers. The article also examines the development work that happens outside Git before code reaches a final commit. ### Source excerpt Zed AI engineer Anant Goel on running evals for every model release, why most eval metrics are vanity, and the work that happens before the final commit. ## Agentic Coding: Bet on the Primitives DevFeed: [Agentic Coding: Bet on the Primitives]() Original publisher: [Read original article]() Author: Robin Wieruch Published: 2026-07-31T06:00:00Z Content type: opinion Language: en Sources: [Robin Wieruch]() Topics: [agentic-coding](), [coding](), [React](), [SVG]() Tags: [abstraction](), [agentic-coding](), [ai-coding-agents](), [ai-engineer](), [build-vs-buy-components](), [coding](), [d3-vs-recharts](), [opinions](), [react](), [svg]() ### AI overview The article argues that agentic coding lowers implementation costs enough to make owning lower-level primitives more practical. It describes an experiment comparing D3 math primitives with React-rendered SVG against Recharts for custom charts: Recharts reached most of the result faster, but its remaining customization needs and an animation issue led to more workarounds than the primitive-based approach. ### Source excerpt Agentic coding collapses the cost of implementation labor. Why the rational bet is moving from high-level libraries back to primitives you own. ## AI Engineer vs. Platform Engineer: Differences in AI Roles DevFeed: [AI Engineer vs. Platform Engineer: Differences in AI Roles]() Original publisher: [Read original article]() Author: Geeta Kakrani Published: 2026-07-22T00:21:05Z Content type: comparison Language: en Sources: [Google Developer Experts - Medium]() Topics: [Artificial Intelligence](), [AI Platform](), [Prompt Engineering](), [MLOps]() Tags: [agentic-ai](), [ai](), [ai-engineer](), [artificial-intelligence](), [career](), [generative-ai-tools](), [llm](), [mlops](), [platform](), [platform-engineering]() ### AI overview The article compares AI Engineer and Platform Engineer roles amid overlapping job titles. It describes AI Engineers as working on model choice, prompts, and agent behavior, while Platform Engineers focus on the infrastructure and operations needed to run AI systems. ### Source excerpt By Geeta Kakrani (GDE in AI & TPU) Open any job board right now and search "AI." Within a few listings, you'll notice something odd: the roles don't line up. Two postings with almost identical requirements have completely different titles. One company's "AI Engineer" is another company's "AI Platform Engineer" is a third company's "MLOps Engineer." This isn't a small naming quirk. For anyone trying to plan a career -- or even just understand where they fit -- it's a real, growing source of confusion. Not because AI itself is hard to understand, but because nobody clearly explains who is responsible for what anymore. The job title problem nobody talks about You'll find titles like: AI Engineer AI Platform Engineer AI Infrastructure Engineer MLOps Engineer LLM Platform Engineer Applied AI Engineer These titles overlap heavily. They pay in similar ranges. They list nearly identical skills. And most of them didn't exist as separate roles even three years ago. This isn't because companies are confused. It's because the industry is still figuring out what to call a very real, very new job -- one that sits between two older disciplines that used to be completely separate. If you're a student or early-career professional trying to plan your path, this title chaos makes it genuinely hard to know what to learn, what to apply for, and what a company actually expects from you on day one. So what's the actual difference? Strip away the job titles, and there are really two different jobs hiding underneath the AI buzzword. The AI Engineer works on the intelligence itself. Choosing which model to use. Designing prompts and instructions. Deciding how an agent should behave, what it should refuse to do, when it should use a tool versus answer directly. This is the "thinking" layer. The Platform Engineer works on everything that lets that intelligence actually run in the real world. Servers, deployment, scaling, security, monitoring, cost control. This is the layer most people never see ## Pushing software engineering limits with "napkin math" DevFeed: [Pushing software engineering limits with "napkin math"]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-07-21T16:52:07Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [Software Engineering](), [Development](), [cloud-infrastructure](), [Databases](), [AI Development](), [cursor](), [Shopify]() Tags: [ai](), [ai-engineer](), [build](), [cursor](), [engineering](), [programming](), [shopify](), [software](), [software-engineering]() ### AI overview The article examines Simon Eskildsen's approach to software engineering, including algorithmic programming, long-term infrastructure work at Shopify, and "napkin math" for estimating compute and data-transfer limits. It also discusses turbopuffer's origins, Cursor as an early customer, and considerations around venture-capital funding. ### Source excerpt Turbopuffer cofounder Simon Eskildsen on the benefits of longer tenure, using first principles to build durable software - and why founders should be cautious when raising VC money ## A Practical Guide to Choosing Among Six AI Career Roles DevFeed: [A Practical Guide to Choosing Among Six AI Career Roles]() Original publisher: [Read original article]() Author: Prasad Rao Published: 2026-05-07T15:01:02Z Content type: article Language: en Sources: [Big Tech Careers]() Topics: [Tech Careers](), [Artificial Intelligence](), [AI Development](), [MLOps](), [Large Language Model](), [Deployment]() Tags: [2026](), [ai](), [ai-engineer](), [article](), [career](), [deployment](), [llm](), [machine-learning](), [mlops](), [orchestration](), [prompt]() ### AI overview This career guide maps six AI roles--AI Engineer, Machine Learning Engineer, AI Product Manager, AI Platform or MLOps Engineer, AI Forward Deployed Engineer, and AI Solutions Architect or AI Consultant--to different strengths, interests, and working styles. It explains that AI Engineers focus on building features and applications, while Machine Learning Engineers focus on model behavior, data, performance, training, evaluation, and deployment. ### Source excerpt The hottest AI roles in 2026, mapped to your skills, interests, and working style ## Agentic Code Review: Pattern Matching for AI DevFeed: [Agentic Code Review: Pattern Matching for AI]() Original publisher: [Read original article]() Author: Robin Wieruch Published: 2026-03-18T06:50:46Z Content type: tutorial Language: en Sources: [Robin Wieruch]() Topics: [agentic-coding](), [Code review](), [AI Agent](), [engineering-culture](), [Code]() Tags: [agentic](), [agentic-code-review](), [agentic-coding](), [ai-agents](), [ai-code-review](), [ai-engineer](), [code-review](), [coding-patterns](), [github](), [review]() ### AI overview This article explains how teams can scale code review for AI-generated code by documenting project patterns and anti-patterns in structured files. AI agents can use the resulting reference document to review code against team conventions on GitHub and locally before pull requests are created. The approach depends on having consistent architecture and established conventions first. ### Source excerpt How to document project patterns and anti-patterns so AI agents can review code against your team's conventions automatically ... ## What Practical Skills Do AI Engineers Need? DevFeed: [What Practical Skills Do AI Engineers Need?]() Original publisher: [Read original article]() Author: Brian Jenney Published: 2026-03-14T15:44:42Z Content type: opinion Language: en Sources: [Brian Jenney]() Topics: [Artificial Intelligence](), [Building AI Systems](), [Retrieval Augmented Generation (RAG)](), [Full-Stack](), [Web app]() Tags: [ai](), [ai-engineer](), [building-ai-systems](), [full-stack](), [rag](), [software]() ### AI overview The author reflects on designing interviews for an AI engineering replacement role and argues that the job is mainly about integrating AI into practical software. The article emphasizes full-stack development, agents, workflows, RAG pipelines, vector databases, and evaluation systems rather than model training or research. ### Source excerpt Last year I quit an AI startup. ## AI Engineers Should Focus on Systems, Architecture, and Production in 2026 DevFeed: [AI Engineers Should Focus on Systems, Architecture, and Production in 2026]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-01-13T11:03:06Z Content type: opinion Language: en Sources: [Neural Bits]() Topics: [Artificial Intelligence](), [systems](), [agentic workflows](), [Inference](), [AI Strategy]() Tags: [2025](), [2026](), [agentic-workflows](), [ai](), [ai-engineer](), [architecture](), [compute](), [inference](), [production](), [reports](), [research]() ### AI overview This opinion article argues that AI engineers should prioritize system design, architecture, scale, inference, monitoring, testing, and core engineering fundamentals in 2026. It says most organizations remain in research or experimentation, and that production failures often stem from the engineering around AI models rather than the models themselves. ### Source excerpt A no-BS breakdown of where to invest your time, backed by real industry insights. ## An AI Engineer's Guide To Choosing GPUs DevFeed: [An AI Engineer's Guide To Choosing GPUs]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2025-12-07T14:02:40Z Content type: tutorial Language: en Sources: [Neural Bits]() Topics: [GPU](), [Nvidia](), [Hardware](), [Machine learning](), [Artificial Intelligence](), [Inference](), [Large Language Model](), [Blackwell](), [Hopper](), [lora](), [NCCL](), [Kernel]() Tags: [ai](), [ai-engineer](), [architecture](), [blackwell](), [clusters](), [compute](), [data-center](), [deep-dive](), [gpu](), [guide](), [hardware](), [hopper](), [inference](), [llm](), [ml](), [nvidia](), [nvlink](), [pcie](), [software](), [technical]() ### AI overview A technical guide to choosing NVIDIA GPUs for AI workloads. It explains how GPU microarchitecture, memory subsystems, form factors, and interconnects affect capabilities, scaling, training, and inference, and compares consumer and data-center GPUs. ### Source excerpt A deep dive on technical Hardware and Software details of NVIDIA GPUs for AI Workloads. ## Recent Guides on AI Inference and FastAPI Backend Engineering DevFeed: [Recent Guides on AI Inference and FastAPI Backend Engineering]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2025-11-29T14:02:54Z Content type: article Language: en Sources: [Neural Bits]() Topics: [AI Development](), [FastAPI](), [Inference Performance](), [backends](), [model-deployment](), [Pydantic](), [Retrieval Augmented Generation (RAG)]() Tags: [ai-engineer](), [ai-inference](), [deploy](), [fastapi](), [guides](), [inference](), [production]() ### AI overview A curated collection of recent guides for AI engineers. It highlights FastAPI and Pydantic practices for building maintainable AI and ML backends, along with inference engines and serving frameworks for deploying models in production across different scales and infrastructure. ### Source excerpt A curated list of the most actionable guides I've published in the past months. ## How AI Medical Imaging Is Powering Precision Healthcare DevFeed: [How AI Medical Imaging Is Powering Precision Healthcare]() Original publisher: [Read original article]() Author: MARTIN ELIAS COSTA, AI ENGINEER @ TOPTAL Published: 2025-07-18T05:00:00Z Content type: article Language: en Sources: [Toptal Blog]() Topics: [Artificial Intelligence](), [Medical imaging](), [Computer vision](), [Deep neural networks](), [data](), [Development]() Tags: [ai](), [ai-engineer](), [artificial-intelligence](), [computer-vision](), [data](), [development](), [diagnostics](), [healthcare](), [images](), [machine-learning](), [medical-imaging](), [neural-networks]() ### AI overview This article explains how artificial intelligence is transforming medical imaging from image acquisition through diagnosis. It focuses on computer vision, neural networks, medical imaging data, and GPUs, and describes an AI system that analyzes brain MRI scans, identifies demyelinating lesions, measures brain-region volumes, classifies atrophy patterns, and integrates results into electronic health records. ### Source excerpt Artificial intelligence is revolutionizing how medical images are acquired, analyzed, and interpreted. The transformation ushers in a new era of data-driven diagnostics and faster, more personalized patient care. ## AI Engineering Primer DevFeed: [AI Engineering Primer]() Original publisher: [Read original article]() Published: 2024-12-19T00:00:00Z Content type: tutorial Language: en Sources: [Tim Kellogg]() Topics: [AI Engineering](), [Large Language Model](), [LangChain](), [DSPy]() Tags: [advice](), [ai-engineer](), [ai-engineering](), [dspy](), [langchain](), [learning](), [llms](), [prompt-optimization]() ### AI overview A primer on getting up to speed with AI engineering. It discusses the field's rapid evolution and advises readers to be cautious with LangChain tutorials, prompt influencers, and blindly applying traditional machine-learning advice. It mentions alternatives including hand-written code, LlamaIndex, DSPy, Gradio, and vendor SDKs. ### Source excerpt How do you get up to speed with AI engineering? Unfortunately, I don't know of any good consolidated resources, so I'm going to attempt to make one here. My first attempt at this focused more on what an AI engineer is and made only a feeble attempt at providing resources to get started. Let's go! ## Is ML Experience A Liability For AI Engineering? DevFeed: [Is ML Experience A Liability For AI Engineering?]() Original publisher: [Read original article]() Published: 2024-12-10T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [AI Engineering](), [Machine learning](), [Artificial Intelligence](), [Large Language Model](), [Data Science]() Tags: [ai](), [ai-engineer](), [ai-engineering](), [fine-tuning](), [llm](), [ml]() ### AI overview An opinion article examines whether prior machine learning experience can hinder a transition into AI engineering. It presents a more balanced view, describing data-pipeline, UX, and model-centric archetypes and contrasting their approaches to LLMs, fine-tuning, model complexity, and engineering tradeoffs. ### Source excerpt Yesterday I posted here about becoming an AI Engineer and made a statement that prior ML experience is often a liability for transitioning into AI engineering. That turned out to be quite the hot take! In this post I'll incorporate feedback and try to expand that into a more balanced take. I'll expand on the perspective of it being an asset, as well as where it's a liability. ## How Can I Be An AI Engineer? DevFeed: [How Can I Be An AI Engineer?]() Original publisher: [Read original article]() Published: 2024-12-09T00:00:00Z Content type: tutorial Language: en Sources: [Tim Kellogg]() Topics: [AI Engineering](), [AI Development](), [Large Language Model](), [Retrieval-Augmented Generation](), [Data pipelines](), [Embeddings](), [Front end](), [React]() Tags: [ai-engineer](), [ai-engineering](), [data-pipelines](), [embeddings](), [frontend](), [genai](), [llms]() ### AI overview The article explains what AI engineers do, emphasizing that the role involves integrating generative AI models into applications and may include building user interfaces, APIs, and data pipelines. It describes several archetypes, including data-pipeline and UX-focused AI engineers, and discusses skills and experience that may be useful. ### Source excerpt You want to be an AI Engineer? Do you even have the right skills? What do they do? All great questions. I've had this same conversation several times, so I figured it would be best to write it down. Here I answer all those, and break down the job into archetypes that should help you understand how you'll contribute. ## How to develop your skillset for the AI era DevFeed: [How to develop your skillset for the AI era]() Original publisher: [Read original article]() Author: jesalg Published: 2024-04-06T00:00:00Z Content type: tutorial Language: en Sources: [Jesal Gadhia]() Topics: [Software Engineering](), [Artificial Intelligence](), [AI-assisted coding](), [GitHub Copilot](), [User Experience]() Tags: [ai](), [ai-assisted-coding](), [ai-engineer](), [career](), [coding](), [github](), [leadership](), [software-development](), [software-engineering]() ### AI overview This article argues that engineers can remain valuable in the AI era by developing skills beyond code generation. It recommends connecting engineering work to business outcomes, adopting an entrepreneurial mindset, understanding company goals, and building customer empathy through user research and analytics. ### Source excerpt How can engineers ensure their skills keep up with AI advancements?