Enterprise AI Architecture & Modernization
Architecture-level training for integrating AI into existing enterprise estates, covering reference patterns, integration, security and the path from legacy to modern platforms.
View program
Strategic capability program
AI Engineering & Agentic AI
Hands-on engineering training for teams building LLM-powered applications, retrieval systems and agentic workflows that can be tested, observed and operated.
Who this program is for
- Software engineers and tech leads moving into AI-enabled products
- Platform and MLOps teams supporting LLM workloads
- Engineering managers setting standards for AI-assisted development
Prerequisites: Participants should be comfortable writing and reviewing production code in at least one mainstream language.
What your teams will be able to do
- Engineers who can design LLM-based features with clear boundaries, fallbacks and cost awareness.
- Working knowledge of retrieval-augmented generation, tool calling and multi-step agent patterns.
- An evaluation mindset: test sets, regression checks and failure analysis for non-deterministic systems.
- Observability, versioning and release practices suited to AI components.
- Team-level engineering conventions for building and reviewing AI features.
Curriculum
Program modules
Modules are selected and sequenced for your audience. Lab environments, languages and the reference application can be matched to your stack. These are confirmed during scoping.
Module 1
LLM application fundamentals
- Model selection and API patterns
- Prompt and context design
- Structured outputs and validation
Module 2
Retrieval and knowledge systems
- Chunking, embeddings and indexing
- Retrieval quality and re-ranking
- Access control for enterprise content
Module 3
Agentic workflows
- Tool use and function calling
- Planning, memory and state
- Human-in-the-loop and failure containment
Module 4
Evaluation and testing
- Building representative evaluation sets
- Automated and human review
- Regression tracking across model changes
Module 5
Operating AI in production
- Tracing, logging and cost monitoring
- Latency and reliability patterns
- Release and rollback practices
Delivery
Online, onsite or hybrid
Choose the model that fits your teams and locations. We are starting in Chennai, Tamil Nadu.
Instructor-led online
Live, facilitated sessions delivered remotely, with labs and exercises designed for virtual participation.
Suited to
- Distributed or multi-site teams
- Shorter, recurring sessions
- Teams outside Chennai
Onsite at your location
Trainers work with your cohort in person at your premises, which suits workshops and team-based practice.
Suited to
- Intensive workshops
- Architecture and roadmap sessions
- Cohorts based in Chennai
Hybrid
A combination of onsite kickoff or workshops and online follow-up, planned as one continuous program.
Suited to
- Multi-phase capability programs
- Mixed local and remote cohorts
- Reinforcement after workshops
Based in or near Chennai? See corporate training in Chennai.
Frequently asked questions
Which languages and frameworks does the program use?
Labs are adapted to the stack your teams use. We confirm languages, frameworks and model access during needs assessment instead of assuming a fixed toolchain.
Do participants build something real?
Yes. The program is lab-led, and participants build and evaluate working components. Where possible, scenarios reflect your own domain, using data you are permitted to share.
How does this relate to the AI governance program?
Engineering teams often pair this program with AI Governance & Responsible AI so that controls and review practices are built into delivery rather than added later.
Related programs
AI Governance & Responsible AI
Training that helps leaders, risk owners and delivery teams put practical governance around AI: policy, accountability, risk assessment and responsible use.
View program
Request a corporate proposal
Tell us about your team and objectives for AI Engineering & Agentic AI. We will use it to prepare a scoped proposal conversation.