Generative AI Developer
Builds applications and products powered by large language models and other generative AI systems, including LLM pipelines, retrieval-augmented generation, and autonomous agents.
Overview
A generative AI developer designs and builds applications that use large language models and other generative systems as core components, ranging from simple API integrations to complex multi-step agents and retrieval-augmented pipelines. They evaluate and select models, engineer the prompts and system instructions that shape behavior, build the supporting infrastructure for memory and context management, and ensure outputs are evaluated and monitored in production. The role sits at the intersection of software engineering and applied AI, and it evolves rapidly as models and tooling improve.
Beginner roadmap
Phase 1: LLM FundamentalsWeeks 1-4
Learn how large language models work conceptually, practice making API calls, and build simple prompt-driven tools to develop intuition for model behavior.
Phase 2: Core PatternsWeeks 5-10
Study and implement the foundational patterns of generative AI development including RAG, structured output, tool use, and chain-of-thought prompting.
Phase 3: Agents and Complex SystemsWeeks 11-18
Build multi-step agents that use tools and memory, learn to evaluate and debug complex LLM pipelines, and design systems that fail gracefully.
Phase 4: Production and IterationWeeks 19-24
Deploy a complete AI-powered application with monitoring, evaluation, and user feedback loops, and document the architectural and product decisions you made.
Portfolio ideas
- A RAG application that answers questions over a specific document collection, with clear evaluation of accuracy.
- An AI agent that completes a multi-step real-world task using tools and external APIs.
- A structured data extraction pipeline that reliably converts unstructured text into a defined schema.
- A deployed LLM-powered product with monitoring, a feedback mechanism, and documented iteration history.
- A comparison of different models or retrieval strategies on the same task, with honest trade-off analysis.
Salary & sources
Salary ranges vary widely by region, seniority, industry, and company. Check current data on reputable salary aggregators (placeholder - verify before publishing).
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Frequently asked questions
Python is the dominant language for working with LLM APIs and frameworks. JavaScript is also useful for building frontend interfaces. Beyond syntax, you need comfort with API calls, async patterns, data handling, and version control.
Generative AI apps introduce probabilistic behavior that changes how you test and validate. Instead of deterministic outputs, you are managing distributions of possible responses, which requires evaluation frameworks, guardrails, and a different mental model for reliability.
RAG stands for retrieval-augmented generation. It is the pattern of fetching relevant context from a knowledge base and passing it to an LLM so the model can answer questions grounded in specific documents rather than relying solely on training knowledge. It is foundational to most real-world LLM applications.
Start with one to build depth, but design your code to be provider-agnostic where possible. Abstraction layers and clean interfaces make it much easier to switch models or run comparisons as the landscape evolves.
Related career guides
AI Prompt Engineer
Designs, tests, and refines prompts that get reliable, high-quality results from large language models across products and workflows.
Machine Learning Engineer
Builds, trains, and deploys machine learning models and the data pipelines and infrastructure that keep them running reliably in production.
NLP Engineer
Builds systems that understand, process, and generate human language, from text classifiers and named entity recognizers to production-grade language model pipelines.
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