Enterprise AI Engineer Program
Build enterprise-ready AI systems from foundational engineering and GenAI to RAG, multi-agent workflows, production MLOps/LLMOps, cloud AI platforms, security and governance.
Skills built for real-world implementation
GenAI & Prompt Engineering
Learn the concepts, implementation workflow, operational checks and troubleshooting approach.
RAG & Vector Search
Learn the concepts, implementation workflow, operational checks and troubleshooting approach.
Agentic AI & MCP
Learn the concepts, implementation workflow, operational checks and troubleshooting approach.
MLOps & LLMOps
Learn the concepts, implementation workflow, operational checks and troubleshooting approach.
Cloud AI Platforms
Learn the concepts, implementation workflow, operational checks and troubleshooting approach.
Security & Governance
Learn the concepts, implementation workflow, operational checks and troubleshooting approach.
What You Will Actually Build
Enterprise RAG Platform with pgvector, Spring Boot and Langfuse
Guided implementation focused on practical architecture, operations and validation.
Multi-Agent Business Workflow with LangGraph and human-in-the-loop
Guided implementation focused on practical architecture, operations and validation.
Production MLOps Platform with MLflow, drift detection and Terraform
Guided implementation focused on practical architecture, operations and validation.
Course modules
Expand each module to see the major topics covered.
Module 01 — Foundations
- Python for AI engineering
- Bash/Unix, Git and REST APIs
- AWS and Azure essentials
Module 02 — Docker, Kubernetes & Terraform
- Docker for ML workloads
- Kubernetes core objects and GPU workloads
- Terraform for AWS and Azure AI infrastructure
Module 03 — Spring Boot & REST API Publishing
- AI microservice patterns
- Spring Boot backends
- API design, security and streaming LLM responses
Module 04 — Databases for AI
- PostgreSQL with pgvector
- CockroachDB and YugabyteDB
- Cassandra event data
- Redis semantic cache
- Pinecone, Weaviate, Milvus and Qdrant
Module 05 — ML & Deep Learning Essentials
- ML lifecycle and evaluation metrics
- scikit-learn and PyTorch
- Hugging Face Transformers
- LoRA/QLoRA and fine-tuning decisions
Module 06 — LLMs, Prompt Engineering & RAG
- Model landscape and model selection
- Prompt engineering and guardrails
- RAG architecture and chunking
- Embeddings, hybrid search and reranking
- LangChain, LlamaIndex and RAG evaluation
Module 07 — Agentic AI & Multi-Agent Systems
- Agent concepts and planning
- LangGraph, CrewAI and AutoGen
- Tool design and function calling
- Human-in-the-loop workflows
- Model Context Protocol (MCP)
Module 08 — MLOps & LLMOps
- MLflow experiment tracking and registry
- KServe, Triton and vLLM serving
- Deployment/retraining pipelines
- Drift detection and CI/CD/CT
- LLM observability and cost governance
Module 09 — Cloud AI Platforms
- AWS SageMaker and Bedrock
- Azure ML, AI Foundry and Azure OpenAI
- Vertex AI and Gemini
- Kubeflow, KServe and Katib
Module 10 — Security, Governance & Capstone
- OWASP Top 10 for LLM apps
- Secrets and network isolation
- Responsible AI and regulation
- Cost optimization
- Enterprise RAG / Multi-Agent / MLOps capstone
Work with the real technology stack
The program is structured around technologies and operational workflows used in modern environments.
Who should join?
- Database administrators and support engineers
- Cloud, DevOps and platform engineers
- Developers moving into infrastructure or AI engineering
- Working professionals upgrading to modern enterprise technologies
Training approach
- Concept → implementation → troubleshooting
- Hands-on and production-oriented examples
- Structured curriculum with practical labs
Public DBA CENTRE reviews
Practical training and strong technical explanations with attention to real-world implementation.
UrbanPro ReviewStudents highlight hands-on learning, interview preparation and trainer support.
UrbanPro ReviewFeedback commonly mentions useful labs, technical depth and real-time examples.
UrbanPro ReviewCommon questions
Is this a hands-on course?
Yes. The program is designed around implementation, administration, troubleshooting and practical labs.
Can working professionals join?
Yes. Contact DBA CENTRE for the current schedule and delivery mode.
Can I attend a demo before enrolling?
Yes. Use the free-demo form or WhatsApp 90195 78898.
Are batch dates fixed on this page?
Batch schedules change. Please contact us for the latest date and timing.
Start Enterprise AI Engineer Program
Book a free demo and get the latest batch schedule.