AI Infrastructure Engineer

 Bridge the Gap Between AI Models and Production Scale

Transform from a Cloud or AI Engineer into a specialized AI Infrastructure Engineer. Learn to build, deploy, and scale GPU-accelerated systems that power the next generation of Generative AI applications.

Full Course Description

The industry has shifted from building AI models to operationalizing them. While data scientists create models, the AI Infrastructure Engineer is the architect who ensures those models run efficiently, securely, and at scale. This is not a theoretical AI course; it is a rigorous, job-oriented technical program designed for Cloud and AI Engineers who want to dominate the high-demand intersection of DevOps, MLOps, and GPU computing.

In this program, you will move beyond simple API calls to build the actual foundations of AI. You will master the art of GPU workload management, orchestrating massive clusters using Kubernetes, and implementing LLMOps pipelines that allow companies to move from a prototype to millions of users.

You will get hands-on experience with the modern AI stack—from provisioning infrastructure via Terraform and AWS Bedrock to managing high-throughput data streams with Apache Kafka and Spark. You will architect vector database strategies using Pinecone and Weaviate, and build high-performance backends with FastAPI and gRPC.

By the end of this course, you will be equipped to design the entire AI lifecycle: from raw data ingestion and feature stores to model deployment, observability with Prometheus, and the governance of enterprise AI systems. You aren't just learning tools; you are mastering the industry workflows required to sustain production-grade AI workloads.

 Detailed Table of Contents

Module 1: Systems Foundations for AI Infrastructure

• Advanced Linux for AI: Kernel tuning for GPU workloads, SSH, and systemd.

• Production Shell Scripting: Automating infrastructure tasks via Bash.

• High-Performance Programming: Python for AI infra and Java for enterprise backend systems.

• Resource Management: Managing CPU/RAM/Disk I/O for heavy AI workloads.

Module 2: Cloud Infrastructure & IaC (Infrastructure as Code)

• Multi-Cloud Foundations: Deploying AI workloads across AWS (EC2, S3, EKS), GCP, and Azure.

• Infrastructure as Code: Writing modular HCL with Terraform and OpenTofu.

• Cloud-Native Provisioning: Using AWS CloudFormation and AWS CDK for repeatable environments.

• Managed AI Services: Architecting Generative AI solutions using AWS Bedrock.

Module 3: Containerization & AI Orchestration

• Docker for AI: Optimizing NVIDIA-Docker containers for GPU pass-through.

• Kubernetes (K8s) Mastery: Pods, Deployments, and Services for AI apps.

• GPU Scheduling: Implementing K8s device plugins for NVIDIA GPU orchestration.

• Helm Charts: Managing complex AI application deployments.

Module 4: High-Throughput Data Engineering for AI

• Real-time Streaming: Architecting data pipelines with Apache Kafka.

• Distributed Processing: Large-scale data transformation using Apache Spark.

• Workflow Orchestration: Building DAGs for AI data pipelines with Apache Airflow and Dagster.

• Data Lakehouse Integration: Connecting S3/Blob storage to AI training clusters.

Module 5: MLOps Engineering & Lifecycle Management

• Experiment Tracking: Managing model versions and hyperparameters with MLflow and Weights & Biases.

• Feature Store Implementation: Building low-latency feature serving with Feast.

• Model Registry: Automating the transition from Staging to Production.

• CI/CD for AI: Building automated AI pipelines using GitHub Actions and Jenkins.

Module 6: LLMOps & Generative AI Infrastructure

• Orchestration Frameworks: Building complex AI chains with LangChain and LlamaIndex.

• Agentic Workflows: Deploying and scaling AI agent automation.

• Prompt Engineering Infra: Managing prompt versioning and evaluation systems.

• LLM Quantization & Deployment: Strategies for reducing GPU memory footprint.

Module 7: Vector Database Architecture

• Vector Embeddings: Understanding the infra behind high-dimensional data.

• Managed Vector DBs: Implementing and scaling Pinecone.

• Open-Source Vector Stores: Deploying and tuning FAISS, Weaviate, and Chroma.

• Indexing Strategies: Optimizing retrieval latency and recall for RAG systems.

Module 8: High-Performance AI Backends & APIs

• Modern API Frameworks: Building high-concurrency AI endpoints with FastAPI and Spring Boot.

• Communication Protocols: implementing REST for external access and gRPC for internal microservices.

• Async Processing: Using Celery/Redis for long-running AI inference tasks.

• API Gateway Management: Rate limiting and authentication for AI endpoints.

Module 9: AI Observability & AIOps

• Metrics Collection: Implementing Prometheus for GPU and system health monitoring.

• Visualization: Building AI-specific dashboards in Grafana.

• Distributed Tracing: End-to-end request tracking with OpenTelemetry.

• Alerting: Setting up automated triggers for model drift and infra bottlenecks.

Module 10: Scaling, Security & AI Governance

• Scaling Strategies: Horizontal vs. Vertical scaling for LLMs (Model Parallelism).

• AI Security: Implementing guardrails, API security, and data encryption.

• Governance: Role-Based Access Control (RBAC) for AI resources.

• Cost Optimization: Managing GPU spot instances and reducing cloud spend.

 Skills You Will Gain

• Architecting GPU-accelerated compute clusters.

• Building end-to-end LLMOps pipelines for production.

• Provisioning multi-cloud AI infra using Terraform/OpenTofu.

• Managing large-scale vector databases for RAG applications.

• Orchestrating AI containers via Kubernetes.

• Designing high-throughput data pipelines with Kafka and Spark.

• Optimizing model inference latency via gRPC and FastAPI.

• Implementing full-stack observability (Prometheus/Grafana/OpenTelemetry).

• Automating AI deployments via GitHub Actions and Jenkins.

• Scaling foundation models using AWS Bedrock and EKS.

• Managing feature stores for real-time AI serving.

• Applying security and governance to Generative AI systems

 Tools Covered

 Career Outcomes

Target Job Roles:

• AI Infrastructure Engineer

• MLOps Engineer / LLMOps Engineer

• AI Platform Architect

• Cloud AI Engineer

• GPU Systems Engineer

Expected Salary Range:

• India: ?25 LPA – ?60 LPA+

• Global: $140k – $250k+

 Course Features

• Job Support: Dedicated resume building and mock interviews for AI Infra roles.

• Real-World Projects: Build a production-ready RAG pipeline and a GPU-orchestrated LLM cluster.

• Industry Certification: Earn a recognized credential in AI Infrastructure Engineering.

• Expert Mentorship: Weekly 1:1 sessions with senior AI Platform Engineers from Tier-1 tech companies.