AI Engineering Courses
Hands-on paths for consultants and engineers: strategy, implementation, and production systems.
Created by Baljeet Dogra
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Why learn with us
- Production-focused. Learn to build systems that hold up on cost, latency, evaluation, and safety.
- Hands-on projects. Leave with a portfolio you can demonstrate, not a certificate of attendance.
- Expert mentorship. Direct access to an AI engineer with 20+ years of experience.
All courses
Choose the course that matches your career goals and experience level.
LLM Apps: Architecture by Use Case
Fourteen archetypes, each with a reference architecture, failure mode and risk profile. Choose the right one, say why, and name what will go wrong before it does.
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Becoming an AI Principal Engineer
Architect, evaluate, cost and operate a production AI system — then defend it with evidence a hostile panel cannot wave away.
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Scenarios: AI Engineering Under Real Conditions
Every session, something has gone wrong and you decide. Case-method. Incomplete briefs, escalations, and traps where the right move is not to build.
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Applied GenAI Engineering
From API calls to production systems, organised by shipped increments. First deploy in week 4, then every month — seven sprints, seven ships.
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Production Generative AI Systems
Customise LLMs, build evaluated RAG and multi-agent systems, then deploy with monitoring, cost control, and safety.
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Claude AI Engineer
Role-focused execution bootcamp: tool use, agents, RAG, Node.js automation, SFCC context, capstone and interview prep.
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How to Become an AI Engineer
The complete roadmap from zero to hired. Master Python, ML, deep learning, and MLOps.
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Enterprise AI Engineer
Build secure, scalable AI systems for large organisations. Focus on architecture and security.
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The AI Consultant
Master AI strategy, ROI, governance, and implementation. Build defensible business cases.
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Python in Seven Days
A conversion week for senior engineers who already ship in C#, Java, PHP, JavaScript or PowerShell: idiomatic Python, interview answers, and something honest to talk about.
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Python for GenAI
Comprehensive Python foundation designed for GenAI learners. 15+ projects included.
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Hands-on LangChain
Build production RAG systems and AI agents. Deep dive into the LangChain ecosystem.
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Hands-on LangGraph
Build advanced agentic RAG systems. Learn state graphs, conditional routing, and multi-step workflows.
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Mathematics for AI
Linear algebra, calculus, statistics, and optimisation with Python — the maths AI engineering actually uses.
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Python conversion for seniors, structures, engineering habits, and the maths path already on the catalogue.
Python in Seven Days
A conversion week for senior engineers who already ship in C#, Java, PHP, JavaScript or PowerShell: idiomatic Python, interview answers, and something honest to talk about.
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Data Structures & Algorithms
The structures and algorithms AI systems actually sit on: arrays, hashes, graphs, heaps, and complexity you can measure — taught in Python, aimed at engineering work.
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Software Engineering Fundamentals
Testing, Git in a team, reviews, APIs, and a deployment you can roll back. The habits AI features fail without.
View courseIntermediate
Prompts, APIs, vector stores, and contracts for non-deterministic backends.
Prompt Engineering Mastery
Prompts that survive users: structure, tests, adversarial cases, and a harness so a wording change cannot silently regress.
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LLM API Integration
Treat the model as a flaky network service: timeouts, retries, streaming, fallbacks, cost per request, and a dashboard.
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Vector Database Deep Dive
Indexes, HNSW, filtering, hybrid search, and operations: the store under your RAG, not a logo on a slide.
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API Design for AI Systems
Contracts for non-deterministic backends: streaming, jobs, idempotency, errors a client can handle, and versioning that survives a model swap.
View courseAdvanced
Deep learning, vision, NLP, MLOps, and fine-tuning with an honest baseline.
Deep Learning Specialisation
From tensors to trained networks you can explain: architecture, optimisation, regularisation, and a model you train — not a notebook you downloaded.
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Computer Vision
Images as data: classification, detection, segmentation, and modern vision-language — with evals that match the job, not ImageNet folklore.
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NLP Advanced Techniques
Beyond “call the LLM”: tokenisation, sequence labelling, retrieval text, evaluation, and when a specialist model still beats a general one.
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MLOps Deep Dive
Training is not the job. Pipelines, registries, promotion, monitoring, and a rollback when the new model is worse.
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Model Optimisation & Fine-tuning
Quantisation, distillation, LoRA — and an honest verdict on whether the smaller or tuned model was worth it.
View courseEnterprise
Security, Kubernetes, governance, and multi-cloud without a single-vendor trap.
AI Security & Privacy
Prompt injection, data leakage, the lethal trifecta, PII, and controls you can show a security review — not a slide about “responsible AI”.
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Kubernetes for AI Systems
GPUs, queues, autoscaling, and serving graphs on Kubernetes — without turning the cluster into the product.
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AI Governance & Compliance
EU AI Act, ISO 42001, NIST AI RMF — mapped onto a real system: roles, evidence, and a file a reviewer can open.
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Multi-Cloud AI Deployment
AWS, Azure, GCP — managed model APIs, identity, networking, cost, and an exit plan so one cloud is a choice, not a trap.
View courseSpecialised
Agents, custom training, testing, and performance — depth on one job.
AI Agent Development
Build the loop yourself: tools, state, budgets, tracing — and the judgment to ship a pipeline instead when that is the senior call.
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Custom LLM Training
Data, tokenisers, continued training, and the ops tax — enough to know when you should not train, and how to when you must.
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AI Testing & QA
Golden sets, judges you calibrate, property tests, and CI that can fail a prompt or a model — QA for systems that do not always say the same thing.
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AI Performance Optimisation
TTFT, TPOT, cache, batch, route — cut cost and latency with quality inside noise, and prove it on a frozen set.
View courseIndustry
Healthcare, financial services, retail, and manufacturing under real constraints.
Healthcare AI Applications
Clinical and operational AI under real constraints: safety, evidence, privacy, and why a demo on MIMIC is not a deployment.
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Financial Services AI
Credit, fraud, support and ops in a regulated firm: model risk, explainability that is real, and audit trails that survive a second line.
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Retail & E-commerce AI
Search, recommendations, content, and service: retrieval and agents against catalogue reality, margin, and a merchandising veto.
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Manufacturing AI
Quality, maintenance, and documents on the line: vision, time series, and retrieval under safety, latency, and an operator who can override.
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