Generative AI Career Roadmap
By Karthick Selvam · Published
A practical path for engineers and analysts moving into generative AI work.
Foundations
Python, APIs, basic machine learning concepts and how LLMs work: tokens, context windows, sampling, limitations.
Building applications
Prompt design, structured outputs, RAG with embeddings and vector search, tool calling, and simple agents.
Production skills
Evaluation, observability, cost control, security (including prompt injection), and deployment of APIs and services.
Specialisations
Fine-tuning and model optimisation, LLMOps, AI product engineering, or AI governance.
Proving skills
Build two or three end-to-end projects with clear write-ups, ideally solving a real problem.
Frequently asked questions
Do I need a PhD?
No. Most generative AI application roles value strong software engineering over research credentials.
Related programmes
- Agentic AI with LangChain & LangGraph
- Building AI Agents with LangGraph & CrewAI
- Multi-Agent Systems: Design & Orchestration
Turn this into a team capability
Our corporate programmes cover topics like this hands-on, mapped to your stack.
AI & GenAI guides
Generative AI vs Traditional AI: What Is the Difference?
Traditional AI mostly predicts or classifies. Generative AI produces new content. Both have a place in the enterprise.
What Is Prompt Engineering?
Prompt engineering is the practice of writing instructions that get reliable, useful output from a language model.
What Is ChatGPT and How Does It Relate to Other AI Assistants?
ChatGPT is a conversational AI assistant from OpenAI, built on large language models. It is one of several similar assistants.