A modern AI engineering track focused on retrieval, observability, inference, evaluation, and the platform choices behind LLM applications.

Understand vector embeddings, similarity search, and vector databases. Build semantic search, recommendation systems, and RAG pipelines using pgvector, Pinecone, and OpenAI embeddings.

AI Infrastructure Mastery

Vector Embeddings: The Foundation of Modern AI Applications

Master the full RAG pipeline for production. Learn about Hybrid Search, Metadata Filtering, and Re-ranking to build AI systems that are both accurate and fast.

Advanced RAG Architecture: Beyond Simple Vector Search

Decide when fine-tuning beats prompt engineering, how to prepare training data, run LoRA fine-tuning efficiently, and evaluate model quality. Covers OpenAI fine-tuning and open-source with Hugging Face.

Fine-Tuning LLMs: When to Fine-Tune, When to Prompt

Production LLM serving: how quantization (GGUF, GPTQ, AWQ) cuts memory by 4x, KV cache memory math, continuous batching in vLLM, TensorRT-LLM for NVIDIA GPUs, and the throughput vs latency trade-offs that determine your serving architecture.

LLM Inference Optimization: Quantization, KV Cache, and High-Throughput Serving

1. Foundations

How to evaluate LLM systems in production: LLM-as-judge patterns with bias mitigation, RAGAS metrics for RAG pipelines (faithfulness, context recall, answer relevancy), BERTScore vs ROUGE trade-offs, building regression test suites for prompts, and the statistical rigor needed to trust eval results.

LLM Evaluation at Scale: LLM-as-Judge, RAGAS, and Building Automated Eval Pipelines

A production guide to LLM observability: OpenTelemetry traces, token and cost metrics, RAG retrieval spans, eval results, safety signals, prompt/version tracking, dashboards, alerts, and redaction patterns.

LLM Observability in Production: Traces, Evals, Cost, Latency, and Failure Modes

2. LLM Operations

A practical backend engineering guide to the Model Context Protocol: host-client-server architecture, tools vs resources vs prompts, JSON-RPC flows, authorization, audit logs, rate limits, idempotency, and production guardrails for AI agents.

MCP for Backend Engineers: Tools, Agents, and Production Guardrails

Stop blindly choosing Pinecone. Learn the technical trade-offs between managed, open-source, and hybrid vector databases for production-grade RAG systems.

AI Masterclass: Vector Database Selection (Pinecone vs. Milvus vs. Weaviate)

3. RAG & Agents

A practical guide to running AI inference on Kubernetes: GPU scheduling, node pools, taints and tolerations, model servers, queue-based autoscaling with KEDA, admission controls, observability, and cost guardrails.

Kubernetes for AI Inference: GPUs, Autoscaling, Queues, and Cost Control

Production AI/ML infrastructure on AWS: SageMaker real-time vs async inference endpoints, EKS GPU scheduling with nvidia device plugin, EC2 GPU instance selection (p4, g5, inf2 Inferentia), Spot instances for training workloads, and the architecture decisions that keep GPU bills under control.

AI Infrastructure on AWS: SageMaker, EKS GPU Scheduling, and Cost-Efficient Inference

Design a practical feature store for production ML systems: offline and online features, point-in-time correctness, streaming updates, Redis/DynamoDB serving, monitoring, and training-serving skew.

System Design: Building a Feature Store for Real-Time Machine Learning

4. Production Infrastructure

Master the economics of LLMs. Learn how to minimize costs and maximize reasoning quality using Context Caching, Prompt Compression, and Surgical Data Injection.

AI Token Usage: The Staff Engineer Guide to Context Optimization

Build production AI agents using Claude's tool use API. Learn the agentic loop, error handling, multi-step reasoning, human-in-the-loop patterns, and how to build reliable autonomous systems.

Building AI Agents with Tool Use: From Chatbot to Autonomous Agent

Go beyond basic prompting. Learn chain-of-thought reasoning, few-shot examples, structured output, self-consistency, ReAct agents, and evaluation techniques for production LLM applications.

Prompt Engineering: Advanced Techniques for Production LLMs

A practical guide to building a Retrieval-Augmented Generation system — from chunking strategies and embedding models to vector databases, retrieval optimization, and avoiding hallucinations.

AI Infrastructure Mastery

A structured path that feels worth paying for

1. Foundations

2. LLM Operations

3. RAG & Agents

4. Production Infrastructure

LLMOps & RAG