To be job‑ready, master the stack that ships real AI: LLMs grounded with retrieval, agents that can act, robust data and MLOps pipelines, and evaluation/safety tooling—plus basics in cloud and privacy.
- Large Language Models (LLMs)
- Use open and hosted LLMs; understand tokenization, context windows, fine‑tuning vs. prompting, latency/cost trade‑offs, and when to prefer small domain models.
- Retrieval‑Augmented Generation (RAG)
- Build grounded apps that cite sources; learn chunking, embeddings, reranking, and hybrid search to boost faithfulness and speed.
- Vector Databases and Search
- Agent Frameworks and Orchestration
- Design tool‑using agents with planning, memory, and guardrails; add human‑in‑the‑loop and audit logs for safe automation.
- Multimodal AI (text, vision, audio)
- Combine modalities for richer apps; build multimodal RAG and teach models to interpret images/plots and respond with text/speech.
- MLOps and LLMOps
- Track experiments, data/model versions, CI/CD, monitoring, eval dashboards; manage prompts, costs, and rollback in production.
- Evaluation and Red‑Team Tooling
- Create offline/online evals for correctness, faithfulness, toxicity, bias; run A/B tests and failure taxonomies; practice model validation.
- Data Engineering for AI
- Ingest, clean, and serve data reliably; build batch/stream pipelines feeding features, retrieval indexes, and analytics.
- Privacy, Security, and Governance
- Apply minimization, masking, policy enforcement, and audit trails; understand compliance needs and AI risk management.
- On‑Device and Edge AI
- Deploy compact models on mobile/edge for low latency and privacy; synchronize with cloud services when online.
How to practice fast (6 mini projects)
- RAG Q&A over course PDFs with citations and reranking.
- Vector search demo with ANN and hybrid BM25+embeddings.
- Tool‑using support agent with human approval and logs.
- Multimodal RAG: answer questions about diagrams/charts.
- LLMOps pipeline: eval harness, monitoring, prompt/version registry.
- Edge demo: on‑device summarizer that syncs with a cloud index.
Bottom line: mastering LLMs, RAG, vector search, agents, multimodal, and MLOps—plus eval, data engineering, governance, and edge—forms the core skill stack that recruiters expect in 2026.
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