Generative & Agentic AI
Generative AI Engineering
Build production applications on large language models — RAG, fine-tuning, evaluation, and beyond.
- Duration
- 14 weeks
- Format
- Online Live
- Level
- Intermediate
- Credential
- Professional Certificate
Program overview
Generative AI has changed what software can do. This program teaches you to build with it responsibly and reliably: retrieval-augmented generation, structured outputs, fine-tuning, evaluation harnesses, and cost/latency engineering. You will ship working GenAI applications, not demos.
What you'll be able to do
- Architect LLM applications with retrieval, tools, and memory
- Build robust RAG systems over private data with proper evaluation
- Fine-tune and adapt open-weight models for specific tasks
- Design evaluation pipelines that catch regressions before users do
- Manage cost, latency, and safety constraints in production
Tools & technologies
- OpenAI & Anthropic APIs
- LangChain
- LlamaIndex
- pgvector
- Hugging Face
- FastAPI
- Ragas
- Docker
Curriculum
5 modules · 14 weeks · every module ends with reviewed applied work.
LLM Foundations for Builders
- How LLMs actually work
- Tokens, context windows, sampling
- Structured outputs and function calling
- Prompt engineering as an engineering discipline
Retrieval-Augmented Generation
- Embeddings and vector databases
- Chunking and indexing strategies
- Hybrid search and reranking
- Evaluating RAG quality
Fine-Tuning & Model Adaptation
- When to fine-tune vs prompt vs RAG
- LoRA and parameter-efficient methods
- Dataset curation for fine-tuning
- Open-weight model deployment
Production GenAI Systems
- Evaluation harnesses and golden sets
- Guardrails, moderation, and safety
- Caching, streaming, and cost control
- Observability for LLM apps
Capstone: Ship a GenAI Product
- Design review
- Build and evaluate a complete application
- Demo day with industry reviewers
Projects & capstone
You graduate with evidence, not just knowledge. Key projects include:
Document intelligence assistant with citation-grounded RAG
Fine-tuned domain model with before/after evaluation report
Capstone: production-ready GenAI application of your design
Who this program is for
- Software engineers adding GenAI to their stack
- Data scientists moving into LLM work
- Technical founders building AI products
Eligibility
Comfortable programming in Python and consuming REST APIs. No prior ML experience required — LLM foundations are taught in-program.
Your instructors
Sofia Marchetti
Head of Generative & Agentic AI Programs
Sofia has been building LLM-powered products since the earliest GPT-3 APIs, including retrieval systems and multi-agent workflows for enterprise clients. She designs curriculum that keeps pace with a field that changes monthly — grounded in patterns that do not.
Daniel Osei
Principal Instructor, Machine Learning Engineering
Daniel built and operated ML platforms serving millions of daily predictions at scale. He teaches machine learning as an engineering discipline — reproducible, tested, monitored — and mentors students through the messy realities of production systems.
Career opportunities
Roles this program prepares you to pursue:
- GenAI Engineer
- LLM Application Developer
- AI Solutions Engineer
- Applied AI Engineer
Earn a verifiable certificate
Complete all required project work to earn the Pioneer Academy Generative AI Engineering certificate — digitally issued with a unique verification link for your CV and LinkedIn.
Program FAQs
Will this stay relevant as models change?
The curriculum focuses on durable patterns — retrieval, evaluation, adaptation, system design — and is refreshed every cohort as the ecosystem evolves.
Are API costs included?
Yes, each student receives credits covering all coursework. Capstones with unusual needs get additional allocation on request.
Take the next step
Start your GenAI & Agents journey
The next Generative AI Engineering cohort is enrolling now. Applications take under ten minutes, and seats are confirmed in order of application.