AI Training in ChennaiAI Engineering Training
High Demand Top Paying AI Role in 2026
AI Engineering Training
in Chennai
Go beyond model building. Learn to design, deploy, and scale production-grade AI systems with MLOps, cloud infrastructure, vector databases, and real-time monitoring.
16 Weeks
Expert Level
₹14–30 LPA
4.9/5 Rating
What you will build
Containerized ML pipeline
CI/CD for ML with GitHub Actions
Production model serving API
Semantic search with vector DB
Multi-cloud AI deployment
Full observability monitoring stack
Course Overview
Duration16 Weeks
LevelExpert (ML & Python needed)
ModeClassroom / Online / Hybrid
Batch DaysWeekday & Weekend
CertificationIndustry Recognized
Placement100% Support
Fee₹38,000 (EMI Available)
Detailed Syllabus
16-Week AI Engineering Curriculum
The most comprehensive AI Engineering program in Chennai from MLOps fundamentals to enterprise-grade production systems.
Module 1 · 2 Weeks
ML Engineering Foundations
Software engineering best practices for ML
ML project structure and packaging
Version control for ML: DVC and Git LFS
Python environments and dependency management
Code quality: testing, linting, type hints for ML
Docker fundamentals for ML applications
Project:Containerized, reproducible ML project template
Module 2 · 3 Weeks
MLOps & CI/CD for Machine Learning
MLOps principles and maturity levels
CI/CD pipelines for ML: GitHub Actions, GitLab CI
ML pipeline orchestration with Airflow & Prefect
Experiment tracking: MLflow and Weights & Biases
Model registry and artifact management
Automated model retraining workflows
Project:End-to-end MLOps pipeline with automated retraining
Module 3 · 3 Weeks
Model Serving & Deployment
Model serving patterns: REST, gRPC, streaming
FastAPI for ML model APIs
TorchServe, TensorFlow Serving, BentoML
Kubernetes for ML workloads
Serverless model deployment
A/B testing and canary deployments for models
Project:Production-grade model serving API with A/B testing
Module 4 · 2 Weeks
Vector Databases & AI Data Infrastructure
Vector embeddings and similarity search
Pinecone, Weaviate, Qdrant, ChromaDB deep-dive
pgvector for PostgreSQL
Data pipelines for AI (ingestion, transformation)
Feature stores: Feast and Tecton
Real-time vs batch data for AI systems
Project:Scalable semantic search engine with vector database
Module 5 · 3 Weeks
Cloud AI Services: AWS, Azure & GCP
AWS SageMaker: training, tuning, deployment
Azure Machine Learning and Azure OpenAI Service
GCP Vertex AI and Gemini API
Cloud cost optimization for AI workloads
Managed vector stores on cloud platforms
Serverless AI with Lambda, Azure Functions, Cloud Run
Project:Multi-cloud AI deployment with auto-scaling
Module 6 · 3 Weeks
Monitoring, Observability & Capstone
Model drift detection and data drift monitoring
Prometheus and Grafana for ML systems
LLM observability: LangSmith, Phoenix, Helicone
AI safety, guardrails, and content moderation
Compliance and responsible AI in production
Capstone: full AI engineering platform
Project:Capstone: production AI platform with full observability stack
AI Engineering Graduates
"The MLOps module is genuinely enterprise-grade. I implemented the same CI/CD pipeline patterns at work that we built during the course. Got a 60% salary hike moving into this role."
Raj Mohan
MLOps Engineer at Amazon
"Cloud AI module covering all three major providers is rare. I was able to architect a multi-cloud AI system for my company. The vector database deep-dive is particularly excellent."
Nithya S.
AI Infrastructure Engineer at HCL
Frequently Asked Questions
What does an AI Engineer do?
An AI Engineer bridges data science and software engineering they build the infrastructure, pipelines, and systems that take AI models from research to production. They handle model serving, MLOps, vector databases, monitoring, and scale.
What background is needed for AI Engineering training?
Solid Python and ML fundamentals are required. Experience with cloud platforms (AWS/Azure/GCP) and basic DevOps concepts are helpful. Our Gen AI or Agentic AI courses are ideal prerequisites.
What cloud platforms are covered in AI Engineering?
You will get hands-on with AWS SageMaker, Azure ML Studio, and GCP Vertex AI. We also cover serverless AI deployment with Vercel, Railway, and Hugging Face Spaces.
What is the salary for AI Engineers in Chennai?
AI Engineers in Chennai earn ₹14–30 LPA. Senior AI Engineers and MLOps specialists can command ₹25–40 LPA, especially with cloud platform expertise.
Become the AI Engineer Companies are Hiring For
Free demo class available. Experience the training quality before committing.
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