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Learn the full AI engineering stack
Strengthen machine learning fundamentals, deep learning, LLM application engineering, retrieval, agents, evaluation, deployment, inference, and production reliability.
MACHINE LEARNING ENGINEERING + AI
Machine Learning & AI Engineering Bootcamp for Software Engineers
A 16-week, part-time online Machine Learning and AI Engineering program for software engineers and technical professionals preparing for Machine Learning Engineer (MLE), AI Engineer, LLM Systems, ML Infrastructure, and Inference Engineering roles.
LLM
RAG
ML
GPU
API
PRODUCTION AI
Model → System → Scale
100% onlineLive, part-time learning
Industry curriculumProduction-first AI skills
Expert mentorshipSenior engineer guidance
Career supportResume, mock + referrals
ABOUT THE PROGRAM
Learn the production skills Machine Learning Engineers and AI Engineers use
Learn the engineering foundations behind modern machine learning systems and apply them through hands-on work in LLM applications, retrieval-augmented generation (RAG), AI agents, model serving, inference optimization, MLOps, and scalable deployment.
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Build systems, not toy notebooks
Design end-to-end ML and LLM applications with APIs, serving layers, caching, observability, evaluation pipelines, cloud infrastructure, and scalable deployment patterns.
03
Turn technical work into interview stories
Translate your projects and prior engineering experience into strong resume bullets, deep-dive narratives, system design discussions, and role-specific interview answers.
WHAT YOU'LL LEARN
AI and Machine Learning skills built for production engineering
The program focuses on the technical areas most relevant to modern Machine Learning Engineer, AI Engineer, ML Systems, and LLM infrastructure work.
Production Machine Learning
Training pipelines, evaluation, feature engineering, experiments, error analysis, monitoring, and reliable model deployment.
LLM Engineering & RAG
Embeddings, retrieval, chunking, vector search, hybrid search, reranking, prompt orchestration, and evaluation.
AI Agents
Tool use, workflow orchestration, memory, state management, fallbacks, guardrails, human-in-the-loop patterns, and observability.
ML System Design
Design scalable recommendation, search, ranking, real-time inference, feature pipelines, and distributed machine learning systems.
Inference & ML Infrastructure
Model serving, batching, scheduling, KV cache, GPU utilization, vLLM, Triton concepts, latency, throughput, and cost tradeoffs.
MLOps & Deployment
APIs, containers, Kubernetes, autoscaling, caching, queues, observability, CI/CD, cloud deployment, and production reliability.
PROGRAM COMPLETION
Finish with a portfolio that proves you can ship AI
Your final work is structured to demonstrate production thinking: model quality, system architecture, latency, scalability, cost, reliability, and measurable outcomes.
See what you’ll build →
solely bootcamp
CERTIFICATE OF COMPLETION
Machine Learning
Machine Learning
Engineering & AI
Presented upon successful completion of the program and final project review.
Production AIML SystemsCareer Ready
CURRICULUM
Machine Learning Engineer curriculum: from ML foundations to production AI systems
The 16-week curriculum is organized as a career transition path for MLE and AI Engineer roles, with deeper emphasis on production machine learning, LLM systems, RAG, inference, deployment, evaluation, and ML system design.
WEEKS 1–3
Machine Learning Foundations
Refresh the concepts that matter for engineering interviews and production modeling without turning the program into an academic survey.
- Supervised learning, regression, classification, ranking and evaluation
- Feature engineering, data quality, leakage and experiment design
- Python ML stack, PyTorch fundamentals and reproducible training
- Model debugging, error analysis and offline/online metric thinking
WEEKS 4–6
Deep Learning & Modern Model Architectures
Build the intuition required to reason about transformers and modern deep learning systems in interviews and real projects.
- Neural network training, optimization, regularization and debugging
- Embeddings, attention, transformers and sequence modeling
- Fine-tuning, LoRA / QLoRA and parameter-efficient adaptation
- Evaluation design for model quality and failure modes
WEEKS 7–10
LLM Applications, Retrieval & Agents
Ship reliable application-layer AI systems instead of stopping at prompt demos.
- RAG architecture, chunking, embeddings, vector search and reranking
- Agent orchestration, tool use, state, memory and workflow design
- LLM routing, fallbacks, guardrails and structured evaluation
- Streaming APIs, FastAPI services and observability
WEEKS 11–14
ML Systems, Serving & Inference
Learn the systems topics that separate production ML engineers from purely modeling-focused candidates.
- Model serving, batching, scheduling, KV cache and latency tradeoffs
- vLLM / Triton concepts, GPU utilization and inference optimization
- Kubernetes, autoscaling, queues, caching, monitoring and reliability
- ML system design for recommendation, search, LLM and real-time inference
WEEKS 15–16 + ONGOING
Interview Preparation & Career Execution
Position your background for the exact role you want and practice the full interview loop.
- Role targeting: MLE, AI Engineer, ML Infra, Inference and AI Systems
- Resume optimization and project deep-dive preparation
- ML coding, system design, behavioral and mock interviews
- Job matching, hiring-network support and referral preparation
HANDS-ON WORK
Hands-on Machine Learning and AI Engineering projects
LLM SYSTEMS
Multi-model AI serving platform
Route requests across models and adapters with streaming, caching, fallbacks, metrics, and latency/cost controls.
Python · FastAPI · vLLM · Redis · Kubernetes
RAG + AGENTS
Enterprise knowledge assistant
Build ingestion, retrieval, reranking, agent workflows, evaluation, observability, and production deployment.
LangGraph · Vector DB · Reranking · Evals
RECOMMENDATION
Real-time ranking system
Design a production recommendation pipeline covering candidate generation, ranking, feature pipelines, serving, and experimentation.
PyTorch · Spark · Kafka · Feature Store
CAREER SUPPORT
Career support for MLE and AI Engineer interviews
Connect your technical work to the way U.S. hiring teams evaluate Machine Learning Engineer and AI Engineer candidates across coding, ML fundamentals, system design, project deep dives, and behavioral interviews.
Talk to admissions01
1-on-1 resume positioning
Rewrite your experience around production ML impact and the role you’re targeting.
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Project deep dives
Prepare architecture, tradeoffs, metrics, failures, scaling decisions, and follow-up questions.
03
Mock interview loops
Practice ML coding, system design, AI/ML knowledge, behavioral, and hiring-manager rounds.
04
Hiring-network support
Prepare for referrals and role matching across relevant AI/ML opportunities.
REQUIREMENTS
Who the AI & Machine Learning Bootcamp is built for
RECOMMENDED BACKGROUND
Designed for technically experienced learners.
- Comfortable programming in Python, Java, C++, JavaScript, or a similar language
- Prior software engineering, data, analytics, research, or technical experience
- Ready to spend consistent part-time hours on lectures, practice, and projects
- Targeting U.S.-style AI/ML engineering interviews or equivalent technical roles
Not sure where you fit? Admissions can help you choose between AI Software Engineer, AI Engineer, MLE, ML Infra, and related paths.
CAREER OUTCOMES
Prepare for production-focused AI and machine learning roles
Build a portfolio and interview narrative around the systems hiring teams expect: model development, LLM applications, retrieval, inference, deployment, monitoring, experimentation, and scalable ML architecture.
MLEMachine Learning Engineer
AIEAI Engineer / AI Software Engineer
ML InfraML Systems & Infrastructure
LLMLLM Systems & Inference
FAQ
Machine Learning & AI Bootcamp FAQ
How long is the program?
The core learning path is structured around 16 weeks of part-time study. Career preparation can continue alongside and after the technical curriculum depending on your plan.
Do I need machine learning experience already?
Prior ML experience helps but is not required for every path. Strong programming or technical experience is the most useful starting point for this advanced program.
Which roles does this program prepare me for?
Common targets include Machine Learning Engineer, AI Engineer, AI Software Engineer, ML Systems Engineer, ML Infrastructure Engineer, Inference Engineer, and production-focused LLM roles.
Is the program live or self-paced?
The program is designed for part-time online learning with live instruction, structured guidance, project reviews, and career preparation so working professionals can study alongside a full-time role.
What happens during career support?
Career support focuses on resume positioning, interview preparation, mock interviews, project deep dives, job targeting, and referral readiness.
START YOUR APPLICATION
Start your Machine Learning or AI Engineering career transition
Tell us about your software engineering or technical background and target role. We’ll use it to help identify the most relevant path across Machine Learning Engineering, AI Engineering, ML Infrastructure, and LLM Systems.