Vasiva Academy

AI Engineering

AI Engineering programs focus on building reliable AI-powered applications: APIs, architecture, retrieval, evaluation, security, and documentation. Shorter programs stay foundational; longer programs introduce advanced topics without claiming mastery of every technology listed.

Python for AI appsAI APIsRAGAgents & toolsEvaluation
Audience

Who this is for

B.Tech / B.E. / MCA students and early-career developers who want to move from Python basics or general programming into building AI-powered applications with evaluation and failure handling.

  • Students and recent graduates exploring AI application engineering
  • Developers with basic programming experience who want structured AI project practice

Prerequisites vary by program. One-month offerings do not promise the same depth as longer programs. Candidates across India may register interest for online programs that match their prerequisites and weekly commitment.

Curriculum

Topics and tools

Foundational and advanced topics appear across durations. Exact tooling is confirmed when a cohort opens.

  • Python fundamentals for AI applications
  • Working with AI APIs
  • Prompt design and structured outputs
  • Basic retrieval patterns
  • Evaluation tests and failure handling
  • Security basics for user data
  • Project documentation and demo
  • Python environment and tooling
  • Data structures for API work
  • HTTP clients and error handling
  • Working with JSON and schemas
  • Basic automated tests
  • LLM application architecture
  • Retrieval-augmented generation basics
  • Evaluation and reliability
  • Security and responsible data handling
  • Tool schemas and validation
  • Bounded agent loops
  • Observability and evaluation
  • Security boundaries for tools
  • Security and data handling
  • Failure modes and fallbacks
  • Testing AI workflows
  • Deployment and operational documentation

Tools

  • Python for AI apps
  • AI APIs
  • RAG
  • Agents & tools
  • Evaluation
  • Responsible data handling

Configured durations: 1 month · 2 months

Expected weekly commitment is published per program and typically ranges from about 8 to 25 hours depending on format.

Projects

Example projects

01

Source-grounded knowledge assistant with evaluation tests

02

Tool-integrated workflow with bounded permissions

Example projects learners may undertake—not published student achievements.

Programs

Published programs in this category

AI EngineeringProject-Based Internship

AI Engineering Internship — 1 Month

An introductory internship focused on building a small, source-grounded AI application with evaluation tests and documented failure handling.

Duration
1 month
Level
Foundational
Delivery
Online
Status
Coming soon — interest registration available

CompensationCompensation terms to be announced

  • Python
  • AI APIs
  • Evaluation
  • Documentation

Illustrative projectBuild a scoped, source-grounded AI application with evaluation checks and documented failure handling.

View details

Assessment: Working project that meets published acceptance criteria · Tests covering core happy path and at least one failure mode · Status: Coming soon — interest registration available

AI EngineeringProject-Based Training

Python for AI Engineering

Focused course on Python patterns used when building AI-powered applications—not a general CS survey.

Duration
1 month
Level
Foundational
Delivery
Online
Status
Coming soon — interest registration available

FeeFee details will be shared before enrollment

  • Python
  • APIs
  • Testing

Illustrative projectBuild a small Python tool that calls an AI API with validated inputs and tested error paths.

View details

Assessment: Exercises and a short capstone script · Passing automated checks where provided · Status: Coming soon — interest registration available

AI EngineeringProject-Based Training

Generative AI Application Development

Build LLM-backed features with structured outputs, retrieval basics, evaluation, and safe data handling.

Duration
2 months
Level
Intermediate
Delivery
Online
Status
Coming soon — interest registration available

FeeFee details will be shared before enrollment

  • LLMs
  • RAG
  • Evaluation
  • Security

Illustrative projectBuild a source-grounded generative feature with evaluation checks and reliability notes.

View details

Assessment: Project rubric · Written reliability notes · Status: Coming soon — interest registration available

AI EngineeringProject-Based Training

AI Agents and Tool Integration

Short course on tool-calling patterns, bounded agent workflows, and evaluation of multi-step behavior.

Duration
1 month
Level
Advanced
Delivery
Online
Status
Coming soon — interest registration available

FeeFee details will be shared before enrollment

  • Agents
  • Tools
  • Evaluation
  • Security

Illustrative projectBuild a tool-integrated assistant with explicit allowlists, bounded loops, and evaluation cases.

View details

Assessment: Project rubric focused on safety and reliability · Status: Coming soon — interest registration available

AI EngineeringProject-Based Training

Production-Oriented AI Engineering

Reliability, evaluation, security, and deployment documentation for AI features intended for real use.

Duration
2 months
Level
Advanced
Delivery
Online
Status
Coming soon — interest registration available

FeeFee details will be shared before enrollment

  • Evaluation
  • Security
  • Testing
  • Operations

Illustrative projectAdd evaluation, failure handling, and operational documentation to an AI feature intended for real use.

View details

Assessment: Reliability and security-focused project rubric · Status: Coming soon — interest registration available

Outcomes

Learning outcomes

  • Implement a scoped AI feature against an external API
  • Write basic evaluation checks for answer quality and failure modes
  • Document architecture decisions and known limitations
  • Write clear Python modules for API-driven workflows
  • Handle errors and structured responses reliably
  • Add basic tests around critical functions
  • Implement a grounded generative feature with evaluation checks
  • Articulate failure modes and mitigations
  • Implement a bounded tool-using workflow with tests
  • Document safety constraints and failure handling
  • Define evaluation criteria for an AI feature
  • Document operational and security constraints
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