ENGINEERING ROADMAP
AI, LLM Integration & Big Data Engineering Roadmap
The definitive engineering path from batch and stream data processing to production AI systems and LLM orchestration. Designed for Data Engineers, ML Engineers, and AI Platform engineers at companies deploying large language models and real-time ML inference pipelines at scale.
AI & Data Engineering Milestones
- Phase 1Data Pipeline FoundationsApache Airflow DAG authoring, dbt transformation models, Medallion (Bronze/Silver/Gold) data lakehouse architecture, and Delta Lake ACID transactions.
- Phase 2Apache Kafka & Stream ProcessingKafka topic configuration, Flink CEP (Complex Event Processing), watermark strategies for late data, and exactly-once stream sinks.
- Phase 3Distributed Compute with SparkSpark Catalyst optimizer, shuffle partition tuning, broadcast joins for skewed data, Delta Lake merge operations, and Databricks Unity Catalog.
- Phase 4Machine Learning PipelinesFeature store design (Feast/Tecton), MLflow experiment tracking, model registry, A/B testing infrastructure, and shadow deployment patterns.
- Phase 5Vector Databases & Semantic SearchHNSW and IVF-Flat indexing algorithms, Pinecone/Milvus/Weaviate collection design, hybrid dense-sparse retrieval, and embedding drift monitoring.
- Phase 6RAG Systems & LLM OrchestrationRetrieval-Augmented Generation pipeline architecture, LangChain/LlamaIndex agentic frameworks, context window management, and hallucination evaluation metrics.
- Phase 7LLM Fine-Tuning & Production ServingLoRA/QLoRA parameter-efficient fine-tuning, vLLM continuous batching inference server, GPU memory quantization (AWQ/GPTQ), and LLM cost monitoring.