Turn model capability into reliable user-facing systems with observability, evaluation and deployment.
NEXT COHORT • ONLINE
AI Engineering
Bootcamp
Build the systems behind modern AI products—from LLM applications and RAG to production inference, ML infrastructure, deployment and evaluation.
This 16-week online AI Engineering and Machine Learning bootcamp is designed for software engineers, data scientists and ML engineers preparing for AI Engineer, Machine Learning Engineer, AI Systems and SDE-AI roles.
LLM SYSTEMS
RAG + AGENTS
ML INFRA
INFERENCE
16Week Part-Time Program
1:1Senior Mentor Guidance
3+Production-Grade Projects
∞Interview & Referral Support
PYTORCH✦LANGGRAPH✦vLLM✦TRITON✦KUBERNETES✦RAG✦LLM EVALUATION✦ML SYSTEM DESIGN✦
PYTORCH✦LANGGRAPH✦vLLM✦TRITON✦KUBERNETES✦RAG✦LLM EVALUATION✦ML SYSTEM DESIGN✦
01 / WHY NOW
AI engineering for the new era.
Think like an engineer.
Modern AI roles demand more than calling an API. The strongest engineers understand model behavior, retrieval, orchestration, serving, reliability, cost, latency and production tradeoffs end to end.
Develop RAG, agents, fine-tuning workflows, routing, memory and structured evaluation pipelines.
Design training, inference and data pipelines with the system tradeoffs expected in senior interviews.
Translate projects into a credible resume story and prepare for coding, ML, system design and deep dives.
02 / THE PROGRAM
AI engineering bootcamp.
Learn. Build. Launch.
This program is built for engineers who want depth. You move from practical ML foundations into modern AI systems, then ship portfolio work you can defend in a senior technical interview.
Get Started ↗Senior Industry Mentors
Learn with experienced engineers who focus on architecture decisions, production tradeoffs and interview depth.
Hands-On System Building
Build deployable services rather than notebook-only demos: APIs, queues, serving, observability and evaluation.
Flexible Part-Time Format
Designed for working engineers with live instruction, recordings, structured milestones and project reviews.
Career Support
Resume positioning, mock interviews, system design coaching and referral support aligned to your target roles.
AI Systems Focus
Go beyond prompt engineering into RAG, agents, inference, ML infrastructure and production AI patterns.
Interview-Ready Deep Dives
Practice explaining design decisions, metrics, bottlenecks, failure modes, experiments and project tradeoffs.
03 / CURRICULUM SUMMARY
AI & ML curriculum: from fundamentals to production AI.
A deliberately engineering-heavy roadmap for AI Engineer, Machine Learning Engineer and AI Systems roles.
See all units ↓01Applied ML FoundationsModels · evaluation · experimentation
02Deep Learning & TransformersPyTorch · attention · embeddings
03LLM EngineeringFine-tuning · PEFT · evaluation
04RAG & SearchRetrieval · reranking · vector systems
05AI AgentsOrchestration · tools · memory · MCP
06Inference EngineeringvLLM · batching · KV cache · routing
07Production ML & MLOpsKubernetes · observability · CI/CD
08ML System DesignScale · reliability · cost · tradeoffs
04 / REAL-WORLD PROJECTS
AI engineering projects you can
defend in an interview.
Each project is structured around real engineering decisions: architecture, data flow, evaluation, latency, cost, scale, failure handling and deployment.
LLM Routing & Serving Platform
Design a production inference layer with model routing, continuous batching, KV-cache optimization, fallbacks and observability.
Enterprise Retrieval Platform
Build hybrid retrieval, reranking, chunking, evaluation and async ingestion for a multi-service knowledge system.
PLAN→ACT→VERIFY
Multi-Agent Orchestration
Create stateful tool-using agents with planning, memory, retries, guardrails, streaming and human-in-the-loop workflows.
05 / CAREER PATHS
Train for roles where AI meets engineering.
Position your background for teams building production machine learning and AI systems.
01
AI Engineer
Ship LLM-powered products, RAG, agents, evaluation and application infrastructure.
↗02
Machine Learning Engineer
Build and productionize models, features, pipelines, serving and monitoring systems.
↗03
ML / AI Systems Engineer
Optimize training, inference, GPU utilization, distributed systems, reliability and cost.
↗04
AI Software Engineer
Blend backend engineering with model integration, orchestration and production AI services.
↗06 / FULL CURRICULUM
Eight-unit AI & ML curriculum.
One complete system view.
The curriculum moves from model fundamentals to production architecture so you can reason across the full AI lifecycle.
UNIT 1
Applied Machine Learning Foundations
Build the model intuition and evaluation discipline needed for production AI: supervised learning, features, metrics, experiments, data quality and error analysis.
MODEL DEVELOPMENTEVALUATIONEXPERIMENTATION
Core overview
Connect ML theory to engineering practice. Learn how training data, offline metrics, serving constraints and business goals interact before a model reaches production.
- Regression & classification
- Tree-based models
- Feature engineering
- Data leakage
- Offline evaluation
- A/B testing
- Error analysis
- Model monitoring
UNIT 2
Deep Learning & Transformer Systems
Understand neural networks, embeddings, attention and transformer architecture well enough to build, debug and discuss modern AI models.
PYTORCHATTENTIONTRANSFORMERS
Core overview
Implement core components in PyTorch, reason about training behavior and connect transformer internals to downstream inference and serving constraints.
- Neural networks
- Backpropagation
- Embeddings
- Self-attention
- Multi-head attention
- Position encoding
- Normalization
- Training loops
UNIT 3
LLM Engineering & Fine-Tuning
Work across the LLM lifecycle: datasets, prompting, fine-tuning, PEFT, quantization, evaluation, model selection and deployment tradeoffs.
LoRA / QLoRAEVALSQUANTIZATION
Core overview
Move from API-level usage into model engineering. Learn how to choose between prompting, retrieval and adaptation based on quality, latency and cost.
- Tokenization
- Prompt design
- Dataset curation
- LoRA / QLoRA
- Quantization
- LLM evaluation
- Guardrails
- Model selection
UNIT 4
Retrieval-Augmented Generation
Design production retrieval pipelines with chunking, embeddings, hybrid search, reranking, context assembly and quality evaluation.
VECTOR SEARCHRERANKINGRAG EVAL
Core overview
Learn why RAG quality is a systems problem. Diagnose retrieval recall, ranking, context composition, freshness and serving latency separately.
- Chunking strategies
- Embeddings
- Vector databases
- BM25
- Hybrid retrieval
- Reranking
- RAG evaluation
- Async ingestion
UNIT 5
AI Agents & Orchestration
Build reliable tool-using workflows with planning, routing, state, memory, retries, streaming, safety and human review.
LANGGRAPHMCPMEMORY
Core overview
Separate agent demos from production workflows. Design explicit state machines, bounded retries, deterministic tools, audit trails and evaluation.
- Agent patterns
- Tool calling
- State machines
- Short/long-term memory
- MCP
- Streaming
- Human-in-the-loop
- Safety & audit
UNIT 6
LLM Inference Engineering
Understand the serving layer behind AI products: batching, KV cache, scheduling, throughput, TTFT, routing, GPU utilization and cost.
vLLMTRITONKV CACHE
Core overview
Reason about inference as a distributed systems and performance problem. Learn the levers that change throughput, latency and unit economics.
- Prefill vs decode
- Continuous batching
- Paged KV cache
- Prefix caching
- Model routing
- Warm pools
- GPU metrics
- Load testing
UNIT 7
Production ML, MLOps & Observability
Deploy and operate AI systems using service architecture, containers, Kubernetes, CI/CD, monitoring, tracing and rollback strategies.
KUBERNETESCI/CDOBSERVABILITY
Core overview
Build production instincts: version everything, observe critical paths, design rollback, control blast radius and understand failure domains.
- Docker
- Kubernetes
- Model registry
- Canary releases
- Prometheus
- Grafana
- OpenTelemetry
- Cost controls
UNIT 8
ML System Design & Interview Execution
Bring everything together through architecture drills, project deep dives, coding, ML fundamentals and end-to-end interview practice.
SYSTEM DESIGNDEEP DIVEMOCK INTERVIEW
Core overview
Practice structured communication under interview constraints. Define requirements, estimate scale, choose models, design data flow and defend tradeoffs.
- Recommendation systems
- Search & ranking
- LLM platform design
- RAG system design
- Feature platforms
- Inference systems
- Resume deep dive
- Mock loops
07 / CAREER ENGINE
AI/ML career support beyond technical depth.
We help you package the work, target the right roles and rehearse the exact interview stories that demonstrate senior-level judgment.
Resume Positioning
Translate your background into role-aligned bullets for MLE, AIE, AI Systems and SDE-AI.
Mock Interviews
Coding, ML fundamentals, system design, behavioral and resume deep-dive practice.
Targeted Referrals
Referral support aligned to fit, readiness and active opportunities across the hiring network.
Offer Strategy
Role selection, interview sequencing, feedback loops and negotiation preparation.
08 / WHO IT'S FOR
Who this AI engineering bootcamp is built for.
The program is especially effective for engineers who want to add credible ML/AI depth without starting their career over.
Software Engineers
Move from backend, distributed systems, cloud or full-stack work into AI product and systems roles.
Data Scientists
Add production engineering, serving, architecture and system design depth to model experience.
ML Engineers
Expand into LLM systems, inference optimization, orchestration and modern AI infrastructure.
09 / FAQ
Questions,
answered.
Do I need prior machine learning experience?+
No advanced ML background is required, but programming experience is strongly recommended. The program starts with practical ML foundations before moving into deep learning and AI systems.
Can I take the program while working full time?+
Yes. The program is designed as a 16-week part-time track with live sessions, recordings and project work that can be completed around a full-time schedule.
Is this more AI Engineer or Machine Learning Engineer?+
It intentionally covers both. The common core is production ML and AI systems; your project and interview emphasis can then be tailored toward AIE, MLE, AI Systems or SDE-AI roles.
What makes the projects different from typical bootcamp projects?+
The projects are designed around architecture and production tradeoffs—not only model training. You are expected to reason about APIs, data flow, serving, latency, reliability, evaluation, monitoring and deployment.
What interview areas are covered?+
Coding, ML fundamentals, ML coding, system design, LLM/AI systems, behavioral questions and detailed project/resume deep dives.
10 / APPLY
Build what the next generation of AI teams needs.
Tell us where you are now and which role you want next. We’ll use that information to evaluate fit for the program.