Data Problem
Enterprise data is fragmented across databases, documents, APIs, data lakes and operational systems.
FLAGSHIP ADVANCED PROGRAMNEXT COHORT - APPLICATIONS OPEN
Build the Data Foundation. Engineer the Intelligence. Deploy the Agents.
Advanced Data Engineering, RAG, Agentic AI, MCP and Production AI taught as one complete engineering discipline.
Go beyond notebooks and chatbots. Learn to build AI-ready data platforms, production RAG systems, tool-using agents, multi-agent workflows and deployable AI applications.
Live CohortHands-On ProjectsProduction ArchitectureMentor Support
Enterprise Data
Data Engineering
AI-Ready Data
RAG / Retrieval
LLM Engineering
Tools / MCP
Agents
Multi-Agent Workflows
Evaluation
Observability
Production AI
12-16 WeeksConfigurable duration
Live Instructor-LedPremium cohort format
5+ Production ProjectsConfigurable project count
Advanced LevelFor working technology professionals
The problem
Modern AI Is Not Just an LLM Problem.
Production AI depends on the quality of the data, retrieval architecture, tools, workflows, evaluation, security and infrastructure around the model.
Engineering Problem
A working AI demo is very different from a reliable production system.
Agent Problem
Agents need tools, state, permissions, guardrails, evaluation and observability before organizations can trust them.
That is why this program starts with data and ends with production agents.
Transformation
Move From Building Pipelines to Building Intelligent Systems.
Traditional Data / Software Professional
SQLPythonETLAPIsSparkCloudBasic MLAnalytics
AI Data / Agentic AI Engineer
AI-ready data platformsAdvanced RAGLLM engineeringTool callingAgent workflowsMulti-agent systemsMCPAI evaluationLLMOpsAI securityProduction deployment
Complete stack
One Program. The Complete AI Engineering Stack.
Layer 01Data Engineering
Layer 02AI-Ready Data
Layer 03Retrieval & RAG
Layer 04LLM Engineering
Layer 05Agents & Tools
Layer 06MCP
Layer 07Multi-Agent Systems
Layer 08Evaluation
Layer 09Security & Governance
Layer 10Production / LLMOps
Architecture
From Enterprise Data to Autonomous Workflows.
UserAI Supervisor
SQL AgentRAG AgentAnalytics Agent
DatabasesVector DBML Models
ToolsMCPExternal Systems / APIsEvaluationHuman ApprovalProduction
UserAI SupervisorSQL AgentRAG AgentAnalytics AgentDatabasesVector DBML ModelsToolsMCPExternal Systems / APIsEvaluationHuman ApprovalProduction
Curriculum
A 12-16 Weeks Engineering Journey
DataAI-ready DataRAGLLMToolsMCPAgentsMulti-AgentEvaluationSecurityLLMOpsProduction
01Modern Data Engineering
Build the data engineering base needed before AI systems can be trusted.
Advanced SQLPython for Data EngineeringData ModelingETL vs ELTBatch ProcessingStreamingData LakesData WarehousesLakehouse ArchitectureApache SparkPySparkKafkaAirflowdbtData QualityData Contracts
Hands-on: Build a production-style data pipeline.
Estimated duration: Configurable
02AI-Ready Data Engineering
Turn enterprise data into reliable inputs for retrieval, search and AI applications.
Unstructured DataDocument IngestionMetadataData ProfilingData LineageDocument ProcessingChunkingEmbeddingsVectorizationSemantic SearchHybrid SearchMetadata Filtering
Hands-on: Build an AI-ready enterprise knowledge pipeline.
Estimated duration: Configurable
03Advanced RAG
Move beyond naive retrieval into robust production-oriented RAG patterns.
Naive RAGAdvanced RAGDense RetrievalSparse RetrievalBM25Hybrid RetrievalRerankingQuery RewritingMulti-Query RetrievalParent-Child RetrievalContext CompressionCitation GenerationGraph RAGAgentic RAG
Hands-on: Build a production-oriented enterprise RAG system.
Estimated duration: Configurable
04LLM Engineering
Design model interactions, structured outputs and provider abstractions with engineering discipline.
LLM APIsPrompt ArchitectureSystem InstructionsStructured OutputsJSON SchemaFunction CallingTool CallingModel SelectionContext WindowsToken EconomicsCachingModel RoutingFallback ModelsProvider Abstraction
Hands-on: Build a structured AI analyst.
Estimated duration: Configurable
05Agent Engineering
Understand when to use agents and how to bound autonomy safely.
Agent vs WorkflowReActPlanningTool UseStateMemorySessionsLoopsRetriesFailure HandlingHuman-in-the-loopBounded Autonomy
Hands-on: Build a tool-using research agent.
Estimated duration: Configurable
06Agent Frameworks
Use frameworks as engineering tools while keeping architecture principles transferable.
LangGraphOpenAI Agents SDKCrewAIStateNodesEdgesConditional RoutingHandoffsAgent-as-tool PatternsMemoryPersistenceGuardrailsTracing
Hands-on: Implement an agent workflow with explicit state and routing.
Estimated duration: Configurable
07Model Context Protocol
Connect agents to tools, resources and systems through MCP concepts.
MCP ArchitectureMCP ClientsMCP ServersToolsResourcesPromptsTool DiscoveryAuthentication ConceptsMCP + DatabasesMCP + APIsMCP SecurityFailure Handling
Hands-on: Build a custom MCP server and connect an agent to it.
Estimated duration: Configurable
08Multi-Agent Systems
Design supervisor, specialist and handoff patterns for complex workflows.
Supervisor ArchitectureRoutingHandoffsAgents as ToolsSequential WorkflowsParallel WorkflowsHierarchical SystemsSpecialist AgentsShared StateFailure RecoveryHuman Approval
Hands-on: Build a multi-agent business analyst with SQL, research, forecast, document and report agents.
Estimated duration: Configurable
09AI + Data Engineering
Use AI to improve data systems, observability, documentation and quality workflows.
Text-to-SQLNatural Language AnalyticsAI Data QualityAI ETLData Pipeline AgentsData Documentation AgentsData Observability AgentsRoot Cause Analysis AgentsData Catalog AgentsSchema UnderstandingData Governance Assistants
Hands-on: Build an AI data quality agent.
Project: Pipeline failure analysis with suggested fixes and human approval.
Estimated duration: Configurable
10AI Evaluation
Evaluate RAG, agents and LLM workflows before scaling them.
Golden DatasetsLLM-as-JudgeRAG EvaluationAgent EvaluationRegression TestingFaithfulnessAnswer RelevancyContext PrecisionContext RecallTool-call AccuracyLatencyCostFailure Analysis
Hands-on: Create evaluation pipelines for RAG and agent behavior.
Estimated duration: Configurable
11AI Security & Governance
Add controls before agents touch tools, data and high-impact workflows.
Prompt InjectionIndirect Prompt InjectionData LeakagePIISecretsTool AbuseExcessive AgencyAuthenticationAuthorizationRBACGuardrailsAudit LoggingData GovernanceModel RiskPrivacy
Hands-on: Secure the multi-agent application.
Estimated duration: Configurable
12Production AI / LLMOps
Package, deploy, monitor and operate AI systems as production-style services.
FastAPIDockerPostgreSQLRedisVector DatabaseAsync APIsBackground JobsRate LimitingCachingLoggingTracingMonitoringToken MonitoringCI/CDSecretsModel RoutingScalability
Hands-on: Deploy the AI platform as a production-style service.
Estimated duration: Configurable
13Cloud & Enterprise Architecture
Reason about cloud deployment, identity, networking, resilience and cost.
Cloud ArchitectureContainer DeploymentStorageNetworking ConceptsIAMSecretsObservabilityScalingCost OptimizationHigh AvailabilityAWSAzureGCPDatabricksSnowflake
Hands-on: Map a production deployment architecture for the capstone.
Estimated duration: Configurable
14Capstone
Deliver an enterprise agentic data intelligence platform with code, docs, evaluation and deployment artifacts.
AI SupervisorSQL AgentRAG AgentForecast AgentData WarehouseVector DBMCPEvaluationHuman ApprovalProduction API
Hands-on: Build the enterprise agentic data intelligence platform.
Project: Source code, README, architecture diagram, API documentation, evaluation report, security checklist, deployment documentation, demo video and repository.
Estimated duration: Configurable
Project portfolio
Build 6 Systems. Graduate With a Portfolio.
Production Data Pipeline
Build a production-style data pipeline for structured analytics and downstream AI use.
PythonSQLSparkAirflowdbt
AI-Ready Enterprise Knowledge Pipeline
Ingest documents, chunk content, create embeddings and support hybrid retrieval.
DocumentsChunkingEmbeddingsVector DBHybrid Search
Advanced RAG Assistant
Implement retrieval, reranking, query rewriting and evaluation.
RAGRerankingQuery RewritingEvaluation
Natural Language to SQL Agent
Build an agent that reasons over data access with structured outputs and tool calling.
LLMStructured OutputSQLTool CallingDatabase
Multi-Agent Business Analyst
Coordinate specialist agents through a supervisor and MCP-connected tools.
SupervisorSQL AgentResearch AgentForecast AgentMCP
AI Data Quality Agent
Detect failures, analyze root causes and route suggested fixes through human approval.
Quality ChecksRoot Cause AnalysisAI RecommendationsHuman Approval
Enterprise Agentic Data Intelligence Platform
Full capstone with data layer, RAG, agents, MCP, evaluation, security and deployment.
CapstoneArchitectureEvaluationSecurityProduction API
Technology stack
Architecture first. Framework second.
Models change. Frameworks change. Engineering principles remain. The program teaches state, tools, orchestration, memory, evaluation, security, observability and deployment beyond one framework syntax.
Primary
PythonSQLPySparkApache SparkKafkaAirflowdbt
AI
LLM APIsEmbeddingsRerankersVector Databases
RAG
Hybrid SearchBM25Dense RetrievalRerankingGraph RAG
Agents
LangGraphOpenAI Agents SDKCrewAI
Protocol
MCP
Backend
FastAPIPostgreSQLRedis
Production
DockerGitGitHubCI/CDCloud
Evaluation
RAGASLLM-as-JudgeCustom Evaluation
Observability
TracingLoggingMetricsCost Monitoring
From Prototype to Production
Anyone can build an AI demo. Production AI requires workflow design, reliability, evaluation, security, observability, cost control and deployment.
AI Systems Must Be Economically Viable.
Learn token economics, model selection, caching, batching, routing, fallbacks, context management, latency and cost per workflow.
Autonomy With Control.
Students learn when agents should act autonomously and when humans must remain in control through approval and audit logs.
Are You Ready for This Program?
Answer each item to see your readiness signal.
Discuss Your Background With an InstructorPrerequisites
Required:
Python fundamentalsSQL fundamentalsBasic GitBasic API concepts
Recommended:
Data Engineering experienceSoftware Engineering experienceCloud familiarityBasic ML concepts
Taught inside:
RAGLangGraphMCPAgentic AI patterns
Learning experience
Built around live engineering practice.
Live Class
Concepts and architecture.
Live Coding
Build core systems together.
Lab
Individual implementation practice.
Project
Production-oriented systems.
Code Review
Review architecture and implementation.
Office Hours
Ask technical questions.
Community
Collaborate with peers.
Weekly Schedule
Monday: Architecture + concepts - Time to be announced
Wednesday: Live coding - Time to be announced
Saturday: Project workshop - Time to be announced
Office Hours: Weekly support - Time to be announced
Cohort Information
Duration: 12-16 Weeks
Format: Live Online
Status: UPCOMING
Outcomes
By the End of the Program, You Should Be Able to...
Build AI-ready data pipelines
Design advanced RAG architectures
Implement hybrid retrieval
Build tool-using AI agents
Design agent workflows
Build multi-agent systems
Create MCP servers
Connect agents to databases and APIs
Implement evaluation pipelines
Add guardrails
Implement human-in-the-loop workflows
Instrument AI systems
Deploy production-style AI APIs
Reason about cost and scalability
Document AI architecture
Capstone
Your Final Project Should Look Like an Engineering System.
Enterprise Agentic Data Intelligence Platform with architecture, agent graph, data layer, RAG layer, MCP layer, evaluation, security, observability and deployment.
GitHubArchitecture diagramREADMEAPIEvaluation reportSecurity checklistDemo
UserAI Supervisor
SQL AgentRAG AgentAnalytics Agent
DatabasesVector DBML Models
ToolsMCPExternal Systems / APIsEvaluationHuman ApprovalProduction
UserAI SupervisorSQL AgentRAG AgentForecast AgentData WarehouseVector DBML ModelsMCPAPIFilesDatabaseEvaluationHuman ApprovalProduction API
Career capability
Relevant to modern AI engineering roles.
Career outcomes depend on your prior experience, portfolio and individual circumstances. There is no guaranteed job placement.
AI EngineerGenAI EngineerAgentic AI EngineerAI Data EngineerData EngineerML EngineerLLM EngineerAI Solutions EngineerAI Platform EngineerAI Architect
Principles
Learn the Principles, Not Just the Tools.
Data before intelligence.
Architecture before framework.
Evaluation before scale.
Security before autonomy.
Observability before production.
Cost before uncontrolled model usage.
Human control for high-impact actions.
Clear boundaries for agent autonomy.
Corporate academy
Train Your AI & Data Engineering Team.
Datamarcos can customize this program for engineering, data and AI teams around your organization's technology stack, cloud environment and business priorities.
CurriculumTechnology stackCloud platformProjectsData environmentSecurity requirementsDurationDelivery formatAssessment
Delivery Options
Live Online
Onsite
Hybrid
Custom Corporate Academy
Book a consultation
Book a Free 1:1 Program Call
Understand your background, goals, program fit, curriculum, cohort and enrollment path. No payment is required for the consultation.
1:1 booking will appear here once Calendly is connected. Contact Datamarcos
Apply for the Program
Enrollment checkout will be enabled soon.
Program investment depends on cohort format and mentorship level.
Testimonials
Professionals Who Have Experienced Datamarcos
Video testimonial slot
Real YouTube, Vimeo or MP4 testimonials will appear here once uploaded.
Real Datamarcos learner and client testimonials will be added after verification.
Verified testimonialRole to be added, Company to be added
FAQ
Questions before you apply.
Who is this program for?
Working technology professionals such as data engineers, software engineers, AI engineers, ML engineers, data scientists, cloud engineers, analytics engineers, backend engineers, technical leads and technology consultants.
Is this a beginner course?
No. This is an advanced engineering program for people who already have technical foundations.
What Python knowledge do I need?
You should be comfortable with Python fundamentals before joining.
Do I need SQL?
Yes. SQL fundamentals are expected because the program connects data engineering and AI systems.
Do I need prior RAG, LangGraph or MCP experience?
No. RAG, LangGraph patterns and MCP concepts are taught inside the program.
Will I build real projects?
Yes. The program is structured around production-oriented systems, labs and a final capstone.
Will I build agents and an MCP server?
Yes. The curriculum includes tool-using agents, multi-agent workflows and a custom MCP server.
Will I learn AI security and evaluation?
Yes. Evaluation, security, governance, human approval and observability are core parts of the program.
Is there job placement?
There is no guaranteed job placement. The program is designed to help you build capabilities relevant to modern AI engineering roles. Career outcomes depend on your prior experience, portfolio and individual circumstances.
Can companies enroll teams?
Yes. Datamarcos can customize this program for corporate engineering, data and AI teams.
Can international students join?
Yes, subject to cohort timing and delivery configuration.
How does the consultation work?
The free 1:1 call helps Datamarcos understand your background, goals and program fit. No payment is required for the consultation.
Can I pay through Razorpay?
Razorpay checkout can be enabled when enrollment is live and a public checkout URL is configured.
What is the refund policy?
Please review the editable refund policy page or contact Datamarcos for the current policy before enrollment.
Ready to Build Production AI Systems?
Talk with Datamarcos about your background, goals and whether this advanced program is the right fit for you.
DataAIAgentsProduction
Do not just learn AI. Learn how to engineer the systems behind it.