AI / ML Engineer Bootcamp

AI / ML Engineer

6 weeks · Intermediate · live + recorded

A hands-on AI/ML curriculum covering Python for ML, scikit-learn fundamentals, deep learning with PyTorch, NLP, modern LLMs (OpenAI, Anthropic), vector databases, RAG patterns, prompt engineering, and MLOps — delivered by senior US ML engineers building real production AI systems.

10Core Modules

35+Practical Skills

5+Project Tracks

1:1Career Support

The production stack you'll master

Each tool taught with the workflows and failure modes practitioners actually meet on the job.

01PythonML-grade Python

02NumPyNumerical computing

03PandasData manipulation

04scikit-learnClassical ML

05PyTorchDeep learning

06Hugging FaceTransformer models

07OpenAILLM API

08AnthropicClaude API

09LangChainLLM orchestration

10Weights & BiasesExperiment tracking

Foundation modules

First half of the curriculum — core fundamentals and the most common day-to-day skills.

ML Foundations

What is ML, supervised vs. unsupervised vs. reinforcement, training/validation/test splits, the bias-variance tradeoff.

Math for ML

Linear algebra essentials, calculus refresher, probability, statistics for ML, gradients and optimization.

scikit-learn

Pipelines, transformers, estimators, linear & tree models, evaluation metrics, cross-validation, hyperparameter tuning.

Deep Learning with PyTorch

Tensors, autograd, neural network basics, training loops, CNNs, RNNs, transfer learning.

NLP Fundamentals

Tokenization, embeddings, word2vec, transformers, attention, BERT, fine-tuning Hugging Face models.

Python EngineersAdd ML and AI to your toolkit — the highest-leverage skill of the decade.

Data ScientistsMove from notebooks into production ML and LLM systems.

Career SwitchersMove into AI/ML from quantitative backgrounds — math, physics, stats.

BuildersShip real LLM-powered products fast.

Advanced modules

Second half — production patterns, real-world operations, and the senior-level depth.

Large Language Models

GPT-4/Claude APIs, prompt design, function calling, structured outputs, evaluation, cost optimization.

RAG & Vector Databases

Embeddings, chunking strategies, Pinecone / Weaviate / pgvector, retrieval quality, hybrid search.

Prompt Engineering & Agents

System prompts, few-shot, chain-of-thought, ReAct, tool use, multi-agent patterns, evaluation.

MLOps

Model versioning, experiment tracking (W&B / MLflow), model registries, deployment (BentoML / SageMaker), monitoring.

Production AI Apps

Building and shipping an AI application end-to-end: backend, vector store, LLM, frontend, deployment, observability.

Hands-on project outcomes

Train a scikit-learn classifier with proper cross-validation.
Fine-tune a Hugging Face transformer on a custom dataset.
Build a RAG application end-to-end with embeddings + LLM.
Deploy an ML model behind a FastAPI endpoint.
Build an LLM agent with tool use and structured outputs.
Run experiment tracking with W&B for a real project.
Build and ship a full production AI application.
Optimize LLM costs through caching, prompt design, and model choice.

Course learnings

Classical ML — when to use which algorithm.
Modern deep learning with PyTorch.
NLP and the transformer architecture.
LLMs in production — RAG, agents, evals, costs.
MLOps — getting models from notebook to production.
Building real AI products that ship.

Career readiness, built in.

techventive.io turns this curriculum into job-ready positioning — project explanation, résumé and LinkedIn rewrites, GitHub portfolio polish, technical interview preparation, and placement-focused guidance through to the offer letter.

AI/ML Engineer
ML Platform Engineer
Applied Scientist
LLM Engineer
MLOps Engineer
AI Solutions Engineer

Who can apply

Freshers, working professionals, managers, career switchers, and aspirants who want a practical, job-focused path.

Python Engineers

Data Scientists

Career Switchers

Builders