India’s Global Capability Centres are hiring AI talent faster than the market can supply it — with gaps of 70–90% in specialist roles like Agentic AI, RAG engineering, and AI governance. These six programmes are built directly from that hiring data: production-grade, project-led, and designed to fit around a working professional’s schedule. Same philosophy as every SparkAI course — you leave with something you built, not just a certificate.
Also open to final-year and graduate students ready to go beyond our foundational tracks.
Generative AI & LLM Engineering
A production-focused programme that takes you from transformer fundamentals to shipping a real LLM-powered application — not just prompting in a playground. You’ll work with embeddings, retrieval, and fine-tuning, and finish with a deployed GenAI app and an evaluation report you can walk a hiring panel through.
Key Outcomes: Transformer & LLM fundamentals • Prompt design and evaluation • Embeddings, vector search, and RAG basics • Fine-tuning with LoRA/PEFT • Ship a working GenAI application with an evaluation report
| Duration | 6–8 Weeks (hands-on labs) |
| Format | Lecture (25%) + Live Coding/Labs (75%) |
| Target Audience | Software engineers, data professionals, and final-year students with working Python |
| Primary Tools | OpenAI / Anthropic / Azure OpenAI APIs, LangChain, Hugging Face, vector databases |
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Agentic AI Systems — Advanced (Multi-Agent Engineering)
The next step after our Agentic AI Bootcamp
Where the bootcamp teaches you to build a single working agent, this track teaches you to orchestrate multiple agents that plan, use tools, and complete multi-step business workflows with minimal supervision — the pattern enterprises are hiring for fastest in 2026. You’ll design state, add human-in-the-loop checkpoints, and deploy an agent system with monitoring.
Key Outcomes: Multi-agent orchestration (LangGraph, CrewAI, AutoGen) • Tool-use and function calling across real systems • State persistence and durable, long-running workflows • Human-in-the-loop safety design • Deploy a monitored multi-agent system
| Duration | 6–8 Weeks (30+ hours of hands-on labs) |
| Format | Lecture (20%) + Live Coding (80%) |
| Prerequisite | Agentic AI Bootcamp or equivalent (Python + one LLM API) |
| Target Audience | Working professionals and graduate students ready to move from single agents to production systems |
| Primary Tools | LangGraph, CrewAI, AutoGen, Claude/OpenAI APIs, Python 3.11+ |
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Prompt Engineering & RAG for Enterprise
Every serious enterprise GenAI deployment — banking, healthcare, retail — runs on retrieval-augmented generation. This is a focused, specialist track: advanced prompting, evaluation frameworks, guardrails, and building a multi-source RAG pipeline that’s benchmarked for accuracy, cost, and latency — the exact skill combination commanding the highest GenAI salaries in India right now.
Key Outcomes: Advanced prompting and in-context learning • RAG architecture and vector database tuning • Building LLM evaluation harnesses • Guardrails and hallucination mitigation • Benchmark a production RAG pipeline for cost and accuracy
| Duration | 4 Weeks |
| Format | Lecture (20%) + Live Coding (80%) |
| Target Audience | AI/software engineers, data scientists moving into GenAI specialist roles |
| Primary Tools | LangChain/LlamaIndex, Pinecone/Weaviate/Chroma, OpenAI/Anthropic APIs |
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MLOps & LLMOps in Production
Models and prompts that work in a notebook aren’t the job — keeping them running reliably in production is. This programme covers the full operational lifecycle: CI/CD for ML, experiment tracking, monitoring, and the LLM-specific discipline of managing cost, latency, and quality once an app is live. You’ll build and deploy a monitored pipeline end to end.
Key Outcomes: CI/CD for ML models • Experiment tracking with MLflow • Containerized deployment and monitoring • LLMOps: cost, latency, and quality monitoring for GenAI apps • Deploy a full pipeline: train → deploy → monitor → retrain
| Duration | 6–8 Weeks |
| Format | Lecture (25%) + Live Coding/Labs (75%) |
| Target Audience | ML engineers, DevOps/cloud engineers, and professionals with prior ML or cloud exposure |
| Primary Tools | MLflow, Docker, Kubernetes basics, AWS/Azure/GCP ML services |
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AI on Cloud (AWS / Azure / GCP)
Cloud is where most GCCs actually run AI at scale — and it’s currently the widest talent gap in the market (55–60% shortage of cloud-native AI expertise). This track takes you hands-on into managed AI services on your chosen cloud: training, hosting, autoscaling, and cost governance, finishing with a deployed model on live cloud infrastructure.
Key Outcomes: Managed training and hosting (SageMaker / Azure AI Foundry / Vertex AI) • Model deployment with autoscaling • Cloud cost governance for AI workloads • Deploy and monitor a model on production cloud infrastructure
| Duration | 6 Weeks per cloud track (choose AWS, Azure, or GCP) |
| Format | Lecture (25%) + Live Labs (75%) |
| Target Audience | Cloud/DevOps engineers, ML engineers, professionals preparing for a cloud AI certification |
| Primary Tools | AWS SageMaker/Bedrock, Azure AI Foundry/OpenAI Service, GCP Vertex AI |
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Applied Data Science with Python — Professional Track
A deeper, production-focused follow-on to our Data Science with Python course
Built for professionals who already know the basics and need to operate at business scale: time-series forecasting, experimentation design, and communicating results to stakeholders — not just building a model, but proving it moved a number.
Key Outcomes: Time-series forecasting for planning and demand • A/B testing and experimentation design • Advanced feature engineering and model evaluation • Present findings as a business-ready stakeholder deck
| Duration | 8 Weeks |
| Format | Lecture (25%) + Live Coding/Labs (75%) |
| Prerequisite | Data Science with Python or equivalent (Pandas, Scikit-Learn basics) |
| Target Audience | Data analysts, BI professionals, and data scientists moving into AI-augmented decision roles |
| Primary Tools | Pandas, NumPy, Scikit-Learn, statsmodels/Prophet, Power BI/Matplotlib |
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