Build a Production-Grade AI Engineering Centre GenAI, MLOps, Governance, and Agentic AI

The EU AI Act has extraterritorial reach it applies to AI systems placed on the EU market or used in the EU, regardless of where the developer is based. If your India GCC builds AI products deployed to EU users, or provides AI components integrated into systems used in the EU, you are likely in scope. Immediate priorities: determine your risk classification (Prohibited/High-Risk/Limited-Risk/Minimal-Risk), understand conformity assessment obligations for high-risk systems, and assess GPAI (General Purpose AI) model obligations if applicable. Our EU AI Act risk classification and conformity assessment framework included in the AI Assurance Kit  compresses this compliance preparation from 6 months to 8 weeks.

These serve different purposes. Databricks is a data and AI platform — best for data engineering (Delta Lake), feature engineering, ML training, and the data lakehouse that feeds AI systems. AWS SageMaker is a managed ML service best for model training, hyperparameter optimisation, model deployment, and MLOps pipeline orchestration on AWS infrastructure. Most mature AI GCCs use Databricks for data and feature engineering, and SageMaker (or Vertex AI on GCP) for model lifecycle management. The choice depends on your primary cloud provider and whether your workloads are data-heavy (Databricks) or ML-serving-heavy (SageMaker).

The five highest-demand AI profiles: (1) LLM engineers with production RAG and fine-tuning experience  most scarce; (2) MLOps platform engineers with Databricks/Kubeflow/SageMaker expertise; (3) AI governance specialists with EU AI Act and NIST AI RMF knowledge new role category, very few practitioners; (4) Data engineers for AI with Delta Lake, dbt, and vector DB experience; (5) Agentic AI developers with LangGraph/AutoGen/CrewAI production deployment experience. All five are significantly more expensive than general software engineering roles.

A production-grade RAG architecture for enterprise has five components: (1) Document ingestion and chunking pipeline (chunk strategy matters fixed vs semantic chunking); (2) Embedding model (text-embedding-ada-002, Cohere Embed, or open-source via Hugging Face); (3) Vector database (Pinecone for managed, Weaviate for self-hosted, pgvector for PostgreSQL-native); (4) Retrieval and ranking layer (hybrid search vector + BM25 keyword, with reranking); (5) LLM response generation with context injection and citation. The failure modes are in the chunking strategy and retrieval quality — not the LLM choice. Our DeepAI team builds and audits RAG architectures as a standalone engagement.