End-to-End AI Engineering
Cover the full AI engineering journey: Python, LLM applications, RAG, AI agents, enterprise integrations, cloud deployment, evaluation and production operations.
LiveInstructor-Led
Forward Deployed AI Engineer
Build LLM applications, RAG systems and AI agents — then scope, ship and prove them inside a real business. The engineering and the customer work, together.
3 Months17 ModulesIntermediate
Next batch in--d:--h:--m:--s
Batch starts 28 November 2026
What You’ll Be Able to Do
Build, integrate, deploy and optimise AI systems against real business problems.
Cover the full AI engineering journey: Python, LLM applications, RAG, AI agents, enterprise integrations, cloud deployment, evaluation and production operations.
Analyse customer requirements, architect the solution, build the POC, deploy to production and prove business impact.
What You Will Learn
Ship real services around a model, with structured output, validation and cost control.
Ingest and chunk real documents, retrieve well, and cite what the answer came from.
Plan, call tools, hold memory, and coordinate several agents on one task.
Connect models to the CRM, helpdesk, databases and webhooks a business already runs.
Choose the data, API and integration shape before writing the code.
Containerise, run on AWS, and ship through CI/CD rather than by hand.
Measure quality, catch hallucination and prompt injection, and watch cost and latency.
Scope it with the customer, prove it in a POC, and measure what it was worth.
Course content
17 modules — from Python and LLM fundamentals to agents, production deployment and customer-facing delivery.
17 sections • 262 lectures
AI Engineering17 modules
- Python Fundamentals
- Advanced Python
- Object-Oriented Programming
- Exception Handling
- File Handling
- Modules & Packages
- Virtual Environments
- Async Programming
- Logging & Debugging
- Type Hints & Dataclasses
- Git & GitHub
- Software Development Best Practices
- Artificial Intelligence Fundamentals
- Machine Learning Overview
- Deep Learning Overview
- NLP Fundamentals
- Generative AI
- Foundation Models
- Large Language Models
- Multimodal AI
- LLM Capabilities & Limitations
- Transformer Architecture
- Attention Mechanism
- Tokens & Tokenization
- Context Windows
- Model Inference
- LLM APIs
- API Authentication
- Model Parameters
- Prompt Engineering
- Prompt Templates
- Few-Shot Prompting
- Context Engineering
- Prompt Chaining
- Structured Outputs
- JSON & JSON Schema
- Pydantic Validation
- Function Calling
- Model Selection
- Token & Cost Optimization
- Prompt Security
- AI Application Architecture
- FastAPI Fundamentals
- REST APIs
- Request & Response Handling
- Pydantic
- API Validation
- Authentication
- Authorization
- JWT
- API Documentation
- Async APIs
- Streaming Responses
- Server-Sent Events
- WebSockets
- AI Backend Architecture
- RAG Fundamentals
- RAG Architecture
- Document Processing
- PDF & DOCX Processing
- Data Cleaning
- Document Ingestion
- Chunking Strategies
- Metadata Management
- Embeddings
- Vector Representations
- Similarity Search
- Vector Databases
- FAISS
- Chroma
- PostgreSQL + pgvector
- Semantic Search
- Keyword Search
- Hybrid Search
- Top-K Retrieval
- Metadata Filtering
- Reranking
- Query Rewriting
- Query Transformation
- Multi-Query Retrieval
- Context Compression
- Parent-Child Retrieval
- Citation-Based RAG
- Agentic RAG
- RAG Optimization
- RAG Evaluation
- AI Agent Fundamentals
- LLM Applications vs Workflows vs Agents
- Agent Architecture
- Agent Planning
- Agent Reasoning
- Agent Execution
- Tool Calling
- Function Calling
- Tool Design
- Tool Selection
- Tool Execution
- Agent State Management
- Agent Control Flow
- Agent Memory
- Agent Error Handling
- Short-Term Memory
- Long-Term Memory
- Persistent Memory
- Agent Workflows
- Sequential Workflows
- Parallel Workflows
- Conditional Workflows
- Routing
- Task Delegation
- Human-in-the-Loop
- Multi-Agent Systems
- Supervisor & Worker Agents
- Agent Coordination
- Agent Reliability
- Agent Evaluation
- LangChain
- LangGraph
- OpenAI Agents SDK
- Agent Orchestration
- Model Context Protocol (MCP)
- MCP Architecture
- MCP Clients
- MCP Servers
- MCP Tools
- MCP Resources
- Connecting Agents to External Systems
- Tool & Resource Management
- AI Evaluation Fundamentals
- Evaluation Datasets
- Golden Datasets
- Test Cases
- Regression Testing
- LLM Evaluation
- RAG Evaluation
- Agent Evaluation
- Hallucination Detection
- Faithfulness
- Response Quality
- AI Guardrails
- Input Validation
- Output Validation
- PII Protection
- Prompt Injection
- Jailbreak Prevention
- Data Leakage
- AI Security
- SQL Fundamentals
- PostgreSQL
- Database Design
- Relationships
- Joins
- Indexing
- Transactions
- PostgreSQL for AI Applications
- pgvector
- Redis
- Caching
- Session Management
- REST API Integration
- Webhooks
- OAuth Fundamentals
- Enterprise API Integration
- CRM Integration
- Helpdesk Integration
- Email & Communication APIs
- Docker Fundamentals
- Docker Images
- Containers
- Dockerfile
- Docker Compose
- Container Networking
- Cloud Computing Fundamentals
- AWS Fundamentals
- IAM
- EC2
- S3
- RDS
- VPC
- Load Balancer
- CloudWatch
- Secrets Management
- CI/CD
- GitHub Actions
- Application Deployment
- Kubernetes Fundamentals
- Kubernetes Architecture
- Pods
- Deployments
- Services
- ConfigMaps
- Secrets
- Scaling
- Production Deployment
- Scalability
- Reliability
- Availability
- Performance Optimization
- Logging
- Monitoring
- Metrics
- Tracing
- Observability
- AI Cost Optimization
- Latency Optimization
- Error Handling
- Retry & Fallback Strategies
- Introduction to Forward Deployment
- Role of a Forward Deployed AI Engineer
- Customer-Facing Engineering
- Customer Discovery
- Stakeholder Identification
- Requirement Gathering
- Business Workflow Analysis
- Problem Definition
- Pain Point Identification
- AI Use-Case Identification
- AI Feasibility Analysis
- Business Value Analysis
- Technical Requirements
- Functional Requirements
- Non-Functional Requirements
- Solution Architecture
- AI System Architecture
- LLM Architecture
- RAG Architecture
- Agent Architecture
- Data Architecture
- API Architecture
- Integration Architecture
- System Design
- Data Flow Diagrams
- AI Workflow Design
- Technical Scoping
- Project Scope
- Dependencies
- Assumptions
- Constraints
- Risks
- Acceptance Criteria
- POC Development
- MVP Planning
- POC to Production
- Enterprise AI Deployment
- Production Rollout
- Integration Testing
- Performance Testing
- User Acceptance Testing
- Customer Onboarding
- Technical Documentation
- User Training
- Knowledge Transfer
- Customer Feedback
- AI Adoption
- Usage Monitoring
- Workflow Optimization
- Continuous Improvement
- Business Impact Measurement
- Productivity Measurement
- Automation Metrics
- Cost Savings
- ROI Measurement
Tools & Technologies
What You’ll Actually Use
Programming & Backend
- Python
- SQL
- FastAPI
- REST APIs
- Pydantic
Generative AI & LLM
- LLM APIs
- Open-Source LLMs
- Prompt Engineering
- Structured Outputs
- Function / Tool Calling
- Embeddings
- Multimodal AI
RAG & Vector Databases
- LangChain
- FAISS
- Chroma
- PostgreSQL
- pgvector
- Vector Databases
- Semantic & Hybrid Search
ChromaAgentic AI
- LangGraph
- OpenAI Agents SDK
- AI Agents
- Multi-Agent Systems
- Agent Memory
- Tool Calling
- MCP (Model Context Protocol)
OpenAI Agents SDK
MCP (Model Context Protocol)Database & Integration
- PostgreSQL
- Redis
- REST APIs
- Webhooks
- OAuth
- CRM & Enterprise API Integration
OAuthProduction AI
- AI Evaluation
- Guardrails
- AI Security
- Logging
- Monitoring
- Observability
- Cost Optimization
- Performance Optimization
Cloud & DevOps
- Git & GitHub
- Docker
- GitHub Actions
- AWS
- EC2
- S3
- RDS
- IAM
- CloudWatch
- Load Balancer
- Kubernetes Fundamentals
Git & GitHub
GitHub Actions
EC2
S3
RDS
IAM
CloudWatch
Load BalancerEnrollment
Choose Your Plan
Flexible payment options to start your journey today.
Full Payment
₹20,000
- Details: Pay upfront and secure your spot in the course.
Limited seats · enroll today
Installment Payment
Two installments of ₹10,000 each
- First Installment: Due at the time of registration.
- Second Installment: Due within 30 days of the course start date.
Limited seats · enroll today
6 hands-on projects · Portfolio-ready
Build real-world projects
A portfolio of production-grade projects you can showcase to employers — not just theory.
Project 1
LLM-Powered Support Assistant API
Beginner6 build steps
Tech you’ll use
Python
FastAPI
Pydantic
DockerBuild a production-shaped FastAPI service around an LLM that answers support questions and returns structured, validated output rather than free text.
Project 2
Enterprise RAG over Internal Documents
Beginner6 build steps
Tech you’ll use
Python
LangChain
PostgreSQL
pgvectorIngest a real document corpus — PDFs, DOCX, messy exports — and answer questions over it with citations the reader can check.
Project 3
Advanced Retrieval: Hybrid Search & Reranking
Intermediate6 build steps
Tech you’ll use
Python
FAISSPostgreSQL
RedisTake the RAG system past the point where plain similarity search stops working, and prove the improvement with numbers.
Project 4
Multi-Agent Workflow with Tools and MCP
Intermediate6 build steps
Tech you’ll use
Python
LangGraphPostgreSQLRREST APIs
Build an agentic system that plans, calls tools, keeps memory across steps, and hands off between a supervisor and workers.
Project 5
Production AI Platform on AWS
Advanced6 build steps
Tech you’ll use
Docker
Kubernetes
AWS
GitHubDeploy the AI service to AWS with the evaluation, guardrails and observability a production system needs.
Project 6
Forward Deployment: Scope, Ship and Prove It
Advanced6 build steps
Tech you’ll use
PythonPostgreSQLRREST APIs
AWSTake a business workflow from first conversation to measured impact — the work a forward deployed engineer is actually hired for.
Roles This Prepares You For
The programme opens up roles across AI engineering, applied AI, agentic systems and AI deployment.
Forward Deployed AI Engineer
- AI Engineer
- Generative AI Engineer
- LLM Engineer
- Applied AI Engineer
- AI Application Engineer
- Agentic AI Engineer
- AI Solutions Engineer
- RAG Engineer
- AI Automation Engineer
- AI Integration Engineer
- AI Platform Engineer
- AI Solutions Architect
Loved by learners
Trusted by 500+ professionals
4.6(500+ reviews)•50+ batches completed
Learners from TCS, Infosys, Wipro, HCL, Cognizant & more
See our Google Reviews4.6“The Linux course is incredibly well structured. Went from zero to confidently managing servers in a week.”
S
Santhosh
DevOps Engineer
“Best free DevOps resource I have found. The quizzes after each topic really cement the knowledge.”
R
Radhika
Cloud Architect
“The community forum is gold. Got my Ansible playbook issue resolved in under 30 minutes.”
G
Geetha
SRE
“SRTechOps is my go-to for interview prep. The Q&A forum has real-world questions that actually appear in interviews.”
V
Vignesh
Platform Engineer
“Cleared my AWS Solutions Architect exam after going through the AWS course here. Highly recommended.”
K
Karthick
AWS Engineer
“Corporate training for my team was excellent. Practical, hands-on and tailored to our stack.”
J
Jegadhish
DevOps Lead
“The Kubernetes deep dives are fantastic. Finally understood how to debug failing pods like a pro.”
BK
Bala Krishna
Kubernetes Engineer
“Loved how every concept comes with a hands-on lab. Theory plus practice made it stick.”
N
Nethra
DevOps Engineer
“The Terraform module helped me automate our entire infra. Saved hours of manual work every week.”
M
Manikandan
Cloud Engineer
“The Linux course is incredibly well structured. Went from zero to confidently managing servers in a week.”
S
Santhosh
DevOps Engineer
“Best free DevOps resource I have found. The quizzes after each topic really cement the knowledge.”
R
Radhika
Cloud Architect
“The community forum is gold. Got my Ansible playbook issue resolved in under 30 minutes.”
G
Geetha
SRE
“SRTechOps is my go-to for interview prep. The Q&A forum has real-world questions that actually appear in interviews.”
V
Vignesh
Platform Engineer
“Cleared my AWS Solutions Architect exam after going through the AWS course here. Highly recommended.”
K
Karthick
AWS Engineer
“Corporate training for my team was excellent. Practical, hands-on and tailored to our stack.”
J
Jegadhish
DevOps Lead
“The Kubernetes deep dives are fantastic. Finally understood how to debug failing pods like a pro.”
BK
Bala Krishna
Kubernetes Engineer
“Loved how every concept comes with a hands-on lab. Theory plus practice made it stick.”
N
Nethra
DevOps Engineer
“The Terraform module helped me automate our entire infra. Saved hours of manual work every week.”
M
Manikandan
Cloud Engineer
“Monitoring with Prometheus and Grafana finally clicked. The dashboards walkthrough was spot on.”
HK
Harish Kumar
SRE
“The CI/CD pipeline course with Jenkins and Argo CD is the most practical I have come across.”
RM
Ravi Mohan
Platform Engineer
“Switched careers into DevOps thanks to SRTechOps. The roadmap kept me focused the whole way.”
P
Praveena
DevOps Engineer
“The AWS networking section cleared up VPCs and subnets for me once and for all. Brilliant content.”
A
Aravind
AWS Engineer
“Live community events are a huge value add. Learned real war stories you will not find in docs.”
C
Charan
Cloud Architect
“Docker finally made sense after this course. Containerised my side projects the very same week.”
V
Vishal
DevOps Engineer
“Cleared my CKA exam with confidence. The mock scenarios mirrored the real thing closely.”
DK
Deepak Kumar
SRE
“The Azure track is underrated gold. Set up my first AKS cluster following along step by step.”
U
Udhaya
Cloud Engineer
“Interview prep here landed me a 40% hike. The questions were exactly what I got asked.”
Y
Yogeshwaran
Platform Engineer
“Monitoring with Prometheus and Grafana finally clicked. The dashboards walkthrough was spot on.”
HK
Harish Kumar
SRE
“The CI/CD pipeline course with Jenkins and Argo CD is the most practical I have come across.”
RM
Ravi Mohan
Platform Engineer
“Switched careers into DevOps thanks to SRTechOps. The roadmap kept me focused the whole way.”
P
Praveena
DevOps Engineer
“The AWS networking section cleared up VPCs and subnets for me once and for all. Brilliant content.”
A
Aravind
AWS Engineer
“Live community events are a huge value add. Learned real war stories you will not find in docs.”
C
Charan
Cloud Architect
“Docker finally made sense after this course. Containerised my side projects the very same week.”
V
Vishal
DevOps Engineer
“Cleared my CKA exam with confidence. The mock scenarios mirrored the real thing closely.”
DK
Deepak Kumar
SRE
“The Azure track is underrated gold. Set up my first AKS cluster following along step by step.”
U
Udhaya
Cloud Engineer
“Interview prep here landed me a 40% hike. The questions were exactly what I got asked.”
Y
Yogeshwaran
Platform Engineer
Built For You
Who Is This For?
Whether you're just starting out or levelling up, this course meets you where you are.
System AdministratorsFreshersApplication DevelopersSecurity EngineersCloud ProfessionalsPractitionersSoftware EngineersSite Reliability EngineersLooking for DevOps InternshipSolution ArchitectsInfrastructure ArchitectsTechnical LeadsIndividuals aiming to build credibility as experienced DevOps professionalsAspirants looking to work as DevOps professionals
Live Cohort · Starts 28 November
Start Learning Today
Fill the 1-minute form to reserve your place in the live cohort.
Live
17 Modules
Certificate
4.6 (500+ reviews)•500+ professionals trained
Fill the Enrollment Form Live sessions Instructor-led Capstone projects
Prefer to talk first? Call us at +91 91504 77118
Frequently Asked Questions
Yes. On completion you receive a training and internship certificate that you can add to your resume and LinkedIn.
Yes. All session recordings and materials remain available to you even after the training ends.
Yes. We provide end-to-end placement guidance and post-training support, and you can re-attend any future batch for free.
Forward Deployed AI Engineer
₹20,000 launch offer · live cohort
4.6(500+)500+ trained50+ batches
Live · Instructor-LedForward Deployed AI Engineer
Build LLM applications, RAG systems and AI agents — then scope, ship and prove them inside a real business. The engineering and the customer work, together.
- 3 Months
- 17 Modules
- Intermediate
- Certificate
Next batch in--d:--h:--m:--s
Batch starts 28 November 2026
Learners from TCS, Infosys, Wipro, HCL, Cognizant & more
Real-World Projects
Lifetime Access
Free Support
Certificate
What You’ll Be Able to Do
Build, integrate, deploy and optimise AI systems against real business problems.
Cover the full AI engineering journey: Python, LLM applications, RAG, AI agents, enterprise integrations, cloud deployment, evaluation and production operations.
Analyse customer requirements, architect the solution, build the POC, deploy to production and prove business impact.
What You Will Learn
Ship real services around a model, with structured output, validation and cost control.
Ingest and chunk real documents, retrieve well, and cite what the answer came from.
Plan, call tools, hold memory, and coordinate several agents on one task.
Connect models to the CRM, helpdesk, databases and webhooks a business already runs.
Choose the data, API and integration shape before writing the code.
Containerise, run on AWS, and ship through CI/CD rather than by hand.
Measure quality, catch hallucination and prompt injection, and watch cost and latency.
Scope it with the customer, prove it in a POC, and measure what it was worth.
Enrollment
Choose Your Plan
Flexible payment options to start your journey today.
₹20,000
- Details: Pay upfront and secure your spot in the course.
Reserve your seat at just ₹20,000
Limited seats · enroll today
Two installments of ₹10,000 each
- First Installment: Due at the time of registration.
- Second Installment: Due within 30 days of the course start date.
Reserve your seat at just ₹10,000
Limited seats · enroll today
6 hands-on projects · Portfolio-ready
Build real-world projects
A portfolio of production-grade projects you can showcase to employers — not just theory.
Project 1
LLM-Powered Support Assistant API
Beginner6 build steps
Tech you’ll use
PythonFastAPIPydanticDockerBuild a production-shaped FastAPI service around an LLM that answers support questions and returns structured, validated output rather than free text.
What you’ll build
- 1Design the request and response contract with Pydantic and JSON Schema
- 2Add prompt templates, few-shot examples and a model-selection fallback
- 3Stream responses with server-sent events so the UI responds immediately
- 4Enforce authentication with JWT and document the API
- 5Track token usage and cost per request, and cap both
- 6Containerise the service and run it with Docker Compose
Project 2
Enterprise RAG over Internal Documents
Beginner6 build steps
Tech you’ll use
PythonLangChainPostgreSQLpgvectorIngest a real document corpus — PDFs, DOCX, messy exports — and answer questions over it with citations the reader can check.
What you’ll build
- 1Build the ingestion pipeline: parsing, cleaning and metadata extraction
- 2Compare chunking strategies and measure the effect on answer quality
- 3Store embeddings in PostgreSQL with pgvector and index for similarity search
- 4Return citations alongside every answer, linked to the source passage
- 5Assemble an evaluation set and measure retrieval accuracy and faithfulness
- 6Tune for the questions that fail, not the ones that already work
Project 3
Advanced Retrieval: Hybrid Search & Reranking
Intermediate6 build steps
Tech you’ll use
PythonFAISSPostgreSQLRedisTake the RAG system past the point where plain similarity search stops working, and prove the improvement with numbers.
What you’ll build
- 1Add keyword search and combine it with semantic search into a hybrid retriever
- 2Introduce reranking and measure the change against your evaluation set
- 3Implement query rewriting and multi-query retrieval for vague questions
- 4Use metadata filtering and parent-child retrieval on a large corpus
- 5Cache embeddings and frequent queries in Redis
- 6Report before-and-after retrieval metrics, not impressions
Project 4
Multi-Agent Workflow with Tools and MCP
Intermediate6 build steps
Tech you’ll use
PythonLangGraphPostgreSQLRREST APIs
Build an agentic system that plans, calls tools, keeps memory across steps, and hands off between a supervisor and workers.
What you’ll build
- 1Design and register tools, with schemas the model can select against reliably
- 2Implement the planning and reasoning loop with a hard step limit
- 3Add short-term and persistent memory backed by PostgreSQL
- 4Connect the agent to an external system through Model Context Protocol
- 5Split the work across supervisor and worker agents with explicit coordination
- 6Add a human-in-the-loop checkpoint before any irreversible action
Project 5
Production AI Platform on AWS
Advanced6 build steps
Tech you’ll use
DockerKubernetesAWSGitHubDeploy the AI service to AWS with the evaluation, guardrails and observability a production system needs.
What you’ll build
- 1Build a CI/CD pipeline with GitHub Actions, including a regression suite
- 2Deploy to Kubernetes with ConfigMaps, Secrets and horizontal scaling
- 3Add input and output guardrails, PII protection and prompt-injection defence
- 4Instrument logging, metrics and tracing across the request path
- 5Run a golden-dataset evaluation on every deploy and block on regressions
- 6Optimise latency and cost, with retry and fallback strategies for model outages
Project 6
Forward Deployment: Scope, Ship and Prove It
Advanced6 build steps
Tech you’ll use
PythonPostgreSQLRREST APIs
AWSTake a business workflow from first conversation to measured impact — the work a forward deployed engineer is actually hired for.
What you’ll build
- 1Run discovery against a real workflow and identify who owns the problem
- 2Write the scoping document: assumptions, constraints, risks, acceptance criteria
- 3Judge feasibility honestly, including whether AI is the wrong tool here
- 4Build the POC, then decide explicitly what must change before production
- 5Integrate with the systems the business already runs, through APIs and webhooks
- 6Roll out with onboarding and documentation, then measure adoption and ROI
Course content
17 modules — from Python and LLM fundamentals to agents, production deployment and customer-facing delivery.
17 sections • 262 lectures
- Python Fundamentals
- Advanced Python
- Object-Oriented Programming
- Exception Handling
- File Handling
- Modules & Packages
- Virtual Environments
- Async Programming
- Logging & Debugging
- Type Hints & Dataclasses
- Git & GitHub
- Software Development Best Practices
Tools & Technologies
What You’ll Actually Use
7 categories • 49 tools
- Python
- SQL
- FastAPI
- REST APIs
- Pydantic
Roles This Prepares You For
The programme opens up roles across AI engineering, applied AI, agentic systems and AI deployment.
Forward Deployed AI Engineer
- AI Engineer
- Generative AI Engineer
- LLM Engineer
- Applied AI Engineer
- AI Application Engineer
- Agentic AI Engineer
- AI Solutions Engineer
- RAG Engineer
- AI Automation Engineer
- AI Integration Engineer
- AI Platform Engineer
- AI Solutions Architect
Loved by learnersTrusted by 500+
Trusted by 500+
professionals
4.6(500+ reviews)50+ batches completed
Learners from TCS, Infosys, Wipro, HCL, Cognizant & more
See our Google Reviews4.6Built for you
Who Is This For?
Whether you're just starting out or levelling up, this course meets you where you are.
- System Administrators
- Freshers
- Application Developers
- Security Engineers
- Cloud Professionals
- Practitioners
- Software Engineers
- Site Reliability Engineers
- Looking for DevOps Internship
- Solution Architects
- Infrastructure Architects
- Technical Leads
- Individuals aiming to build credibility as experienced DevOps professionals
- Aspirants looking to work as DevOps professionals
Live Cohort · Starts 28 November
Start Learning Today
Fill the 1-minute form to reserve your place in the live cohort.
Live
17 Modules
Certificate
4.6 (500+ reviews)500+ professionals trained
Fill the Enrollment Form- Live sessions
- Instructor-led
- Capstone projects
Prefer to talk first? Call us at +91 91504 77118
Frequently Asked Questions
Will I get a certificate?
Yes. On completion you receive a training and internship certificate that you can add to your resume and LinkedIn.
Will I have lifetime access to the training materials?
Yes. All session recordings and materials remain available to you even after the training ends.
Is support provided after training?
Yes. We provide end-to-end placement guidance and post-training support, and you can re-attend any future batch for free.