AI Engineering Course: Build, Understand and Ship AI Systems

Yes. The AI Engineering Course with Placement Support is our AI course with placement — a live, project-led career program for graduates and freshers. It covers Python, databases, machine learning, deep learning, GenAI, RAG, AI agents, MCP and production deployment, followed by interview preparation, a project portfolio, a completion certificate and placement support.

An AI Engineering course teaches you to build complete AI systems — from Python and data foundations through machine learning, deep learning, GenAI, RAG, AI agents, MCP and production deployment. The AI Engineering Course at School of Core AI is a live, project-led career program for graduates and freshers, with interview preparation, a project portfolio, a completion certificate and placement support.

Yes. The AI Engineering Course is designed for final-year students, recent graduates and freshers. It starts with Python foundations, so prior AI experience is not required. Consistent coding practice and willingness to work through projects are essential.

Prior AI experience is not required. The program teaches Python from foundations. You need consistent coding practice, willingness to work through projects, basic computer literacy and a laptop with internet access.

Yes. Data structures and algorithms are part of the parallel interview lane in this program. You practice Python coding, SQL, arrays, strings, hash maps, stacks, queues, trees, graphs, recursion and complexity alongside the AI modules.

Yes. Phase 1 covers Python foundations, Git, Linux, NumPy, Pandas, SQL, PostgreSQL, MongoDB, APIs, JSON and data modelling — the engineering base every AI Engineer needs.

Yes. Phase 3 covers LLM fundamentals, prompt design, structured outputs, LLM evaluation, RAG ingestion, chunking, embeddings, vector databases, hybrid retrieval, reranking, grounding, RAG evaluation, tool-calling agents, workflow state, multi-agent concepts, LangGraph and Model Context Protocol.

You build five portfolio projects and one capstone: a Production ML Decision API, a Deep-Learning Vision or Document Intelligence System, an Evaluated LLM Application, an Enterprise Hybrid RAG Knowledge System, an MCP-Based AI Agent Workflow and a Production AI Engineering Capstone. Each project includes a GitHub repository, architecture diagram, evaluation report and demo video.

Yes. Placement support includes profile and skill baseline, GitHub, resume and LinkedIn preparation, coding and technical readiness checks, mock interviews and project defence, and role-matched opportunities and hiring support. Placement support is designed to help learners become application- and interview-ready — it is not a job guarantee.

You receive a Project-Verified AI Engineering Certificate awarded after technical assessments, required project completion, portfolio review, an AI system-design review and capstone defence. The certificate is assessment-led and secondary to the career capabilities you build.

AI Engineering is the broad, fresher-to-career pathway covering Python, databases, ML, deep learning, GenAI, RAG, agents, MCP, production deployment and interview readiness. Generative AI is a focused specialisation going deeper into LLMs, fine-tuning and multimodal AI. AI Engineering is best for graduates and freshers; Generative AI is best for developers and ML learners who already have foundations.

AI Engineering covers the complete AI career pathway from foundations to production. Agentic AI is a focused specialisation going deeper into agents, orchestration and MCP. AI Engineering is best for graduates and freshers starting their career; Agentic AI is best for learners already familiar with LLMs and RAG who want to specialise in autonomous agent systems.

Yes. Phase 3 covers RAG systems (ingestion, chunking, embeddings, vector databases, hybrid retrieval, reranking, grounding, citations, RAG evaluation) and agent workflows (tool-calling agents, workflow state, multi-agent concepts, LangGraph, Model Context Protocol, permissions and human approval). You build a hybrid RAG knowledge system and an MCP-based agent workflow as portfolio projects.

Yes. Phase 4 covers FastAPI, Docker, cloud deployment concepts, logging, tracing, evaluation gates, guardrails, caching, retry and fallback strategies, and latency and cost optimisation. The capstone project requires a deployed AI service with health checks and observability.

The AI Engineering Course includes production literacy — FastAPI, Docker, evaluation gates, tracing, and basic observability. It is not a full MLOps programme. If you want deep CI/CD, model registries, drift detection, orchestration platforms, and scaled operations, the MLOps Course or AIOps Course goes further.

Placement support runs in parallel throughout the program, not as a last-minute crash course. It includes five steps: profile and skill baseline, GitHub and resume preparation, coding and technical readiness checks, mock interviews and project defence, and role-matched opportunities. Placement support is structured guidance to help you become interview-ready — it is not a job guarantee. We do not publish placement percentages, salary claims or hiring-partner logos.

The AI Engineering Course is a live online program. For current duration, schedule, fee and upcoming cohort information, use the "Get Syllabus & Fees" form on this page and our team will share the latest verified details.