AI Engineer — Data (RAG, NL2SQL & GenAI Pipelines on Databricks/Azure)
capability building,
designed for your organisation.
A custom-built corporate programme for Data engineers, ETL/ELT developers, and analytics engineers (4+ years) owning the data-plane of AI products on Databricks, Azure, or Snowflake. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Where structured, semi-structured, and unstructured data each fit in modern AI products
- Ingestion path selection by source type and downstream use case
- When raw documents trump database extracts (and vice versa)
- Lab: catalog a mixed-format corpus and pick ingestion paths
Want the full module-by-module syllabus, sample assignments, and pricing?
One PDF — sent to your inbox in under a minute.
Demonstrable skills your team will apply on live projects.
Build production RAG on Azure Databricks + Unity Catalog
Ingest mixed-format corpora, embed with the right model, ground LLM responses with citations and authorisation.
Ship NL2SQL services with schema grounding
Star-schema-aware NL2SQL agents with few-shot calibration; semantic-layer integration via Cube; chart-generation patterns.
Operationalise GenAI pipelines as scheduled DAGs
Airflow / ADF orchestration with eval-gated CI; index rotation strategies; drift detection on data, embedding, and prompt levels.
Pass the joint capstone evaluation panel
Each engineer ships an end-to-end pipeline on a realistic enterprise dataset, evaluated against quality, cost, and governance dimensions.
Earn the AI Engineer (Data) credential
Cohort first-attempt completion rate of 90%. Two attempts permitted. Built for BFSI, retail, and manufacturing data delivery.
Lead AI delivery on data-heavy client accounts
Data-side AI engineers are increasingly the bottleneck on enterprise GenAI deployments. Alumni typically take an immediate scope leap on Databricks-heavy accounts.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Strong Python and advanced SQL but no production RAG, NL2SQL, or LLM-augmented pipeline experience
- ·Proficient in Databricks, Snowflake, or Synapse but new to vector stores and embedding workflows
- ·Familiar with Airflow / ADF / dbt but haven't orchestrated AI pipelines as scheduled DAGs
- ·Limited understanding of LLM-specific data quality (chunking, metadata, drift detection)
- ·No fluency with NL2SQL architectures, semantic layers (Cube), or insight-narration patterns
- ·Cannot independently apply data governance (Unity Catalog, lineage, PII) to AI/RAG data products
Where they'll arrive
- ✓AI & data engineering at scale — ingestion, transformation, embedding pipelines on Databricks/Fabric
- ✓GenAI solution development — RAG, NL2SQL, and insight services on top of governed data
- ✓Intelligent automation — LLM-powered classification, enrichment, and data-operation automation
- ✓Scalable pipeline architecture — batch, streaming, and real-time architectures for AI
- ✓MLOps & deployment for GenAI — CI/CD for prompts and models, monitoring drift and cost
- ✓Data governance & quality — lineage, catalogs, PII controls, and eval-driven quality gates
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
DevOps focuses on software delivery, MLOps manages ML model pipelines, and AI Ops for data systems emphasizes RAG reliability, NL2SQL accuracy, governance, and AI-driven data operations.