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ProgrammesDiplomaAI Engineering
Diploma
AI Engineering
Build deep, practical expertise in AI engineering, develop the confidence to solve real-world problems, and prepare for the opportunities shaping the future of technology.
1 yearDuration
On CampusFormat
FlexiblePacing
English & UrduLanguage
48Weeks
192Sessions
4Milestone Projects
4Oral Defenses
The Roadmap
What You’ll Learn
A carefully sequenced journey from core engineering foundations to building, deploying, and scaling real AI systems.
What You’ll Learn
Before Month 1 begins, every student proves they can already build — enough Python and software engineering practice to make the rest of the year possible. Only students who pass move into Month 1.
Outline
Week P1 — Python FundamentalsWeek P2 — Working Like an EngineerWeek P3 — APIs & Real Code
Outcomes
Gate Assessment (pass/fail) — build and submit a CLI tool that calls a public API, handles real error cases, is tracked in Git, and includes passing tests. Reviewed by instructor, no partial credit.
Most courses jump straight to "build a chatbot." Here, students understand sampling and context mechanics before they ever touch a framework — so when something breaks in Month 2, they know why.
What You’ll Learn
Tokens, context windows, and sampling parameters; working with Claude and OpenAI-compatible APIs; prompt engineering patterns; and context engineering — the differentiator that explains why RAG works the way it does.
Outline
Week 1 — Orientation & Core LLM MechanicsWeek 2 — Working with Model APIsWeek 3 — Prompt EngineeringWeek 4 — Context Engineering
Outcomes
Milestone 1: a structured-output tool — system prompt, function calling, validated output — defended in a live design walkthrough.
What You’ll Learn
Embeddings and semantic search; indexing and querying a real vector database; building a RAG pipeline from raw chunking through to a framework-based rebuild with LangChain and LlamaIndex.
Outline
Week 5 — EmbeddingsWeek 6 — Vector DatabasesWeek 7 — Building RAG from ScratchWeek 8 — RAG Frameworks & Milestone 2
Outcomes
Milestone 2: a RAG system built over a real Pakistani-context dataset — must explain retrieval failures, not just demo success.
What You’ll Learn
Building AI agents by hand before reaching for an SDK; the Model Context Protocol end to end; evaluation and observability; and the safety practices every production system needs.
Outline
Week 9 — AI AgentsWeek 10 — Model Context Protocol (MCP)Week 11 — Evaluation & ObservabilityWeek 12 — AI Safety, Ethics & Capstone
Outcomes
Oral Defense 1: present and defend a live RAG or agent-based system — why this chunking strategy, why this eval approach, what breaks under adversarial input.
Quarter Outcome
A working system a student built, shipped, and can defend under questioning — not a tutorial they followed.
Entry requires a passed Q1 capstone. Q1 asks "does it work?" Q2 asks "does it work at 2am when traffic spikes, when a user tries to break it, and when the bill triples?"
What You’ll Learn
Wrapping AI logic in a real FastAPI service; containerizing and deploying it publicly; adding Postgres and Redis-backed state; and automating the whole pipeline with CI/CD.
Outline
Week 13 — Serving AI SystemsWeek 14 — Containerization & DeploymentWeek 15 — Databases & State for AI AppsWeek 16 — CI/CD & Infrastructure as Code
Outcomes
Milestone 4: a previous project deployed as a live, authenticated, publicly accessible API — instructor live-tests the endpoint and tries to break it.
What You’ll Learn
Multi-agent orchestration with LangGraph, CrewAI, and AutoGen; long-term agent memory; guardrails and fallback strategies; and hybrid + reranked retrieval for real accuracy gains.
Outline
Week 17 — Multi-Agent Orchestration FrameworksWeek 18 — Agent Memory SystemsWeek 19 — Guardrails, Reliability & Fine-Tuning AwarenessWeek 20 — Advanced Retrieval
Outcomes
Milestone 5: a production-grade multi-agent system with memory and guardrails — instructor triggers a live tool failure and it must degrade gracefully.
What You’ll Learn
Caching and model-routing strategies that actually move a monthly AI bill; dashboards and SLOs; production security hardening; and defending a fully deployed, load-tested system.
Outline
Week 21 — Performance & Cost OptimizationWeek 22 — Monitoring & Observability at ScaleWeek 23 — Security HardeningWeek 24 — Capstone 2 Build & Oral Defense
Outcomes
Oral Defense 2: defend what breaks the system, what it costs to run at scale, and what changes at 10x users.
Quarter Outcome
A student who can take a working AI prototype and turn it into something a company would actually deploy — the gap between a portfolio demo and a system a company trusts in production.
Entry requires passed Q1 and Q2 capstones. Real AI Engineer roles fork this way in practice — a platform hire, a product hire, and a research-leaning hire do genuinely different daily work under the same job title.
What You’ll Learn
Kubernetes for AI workloads, model serving at scale with vLLM/TGI, unified LLM gateways, and building internal developer platforms other teams plug into.
Outcomes
Comfortable orchestrating AI workloads and routing traffic across multiple model providers.
What You’ll Learn
Multi-region deployment and failover, GPU resource and cost governance, building observability platforms from scratch, and chaos engineering.
What You’ll Learn
Build and defend a full internal AI platform — self-serve access, usage tracking, per-team budgets — as if presenting to a Head of Engineering.
Outcomes
Capstone Defense: job-ready for AI Platform Engineer or Infrastructure Engineer roles — the person other engineers depend on to keep AI systems running.
Quarter Outcome — All Lanes
A shared AI Engineer foundation plus a specialized edge that maps to how hiring managers actually differentiate candidates — not just "knows AI," but "is the platform person / the product person / the research-leaning person" on a team.
Not new technical content — this is where everything from Q1–Q3 gets pointed at the outside world: real clients, a real portfolio, real interviews, a real income path.
What You’ll Learn
Working like a real dev team — sprint planning, code review culture — then live, paired work on an actual client engagement with real deadlines and real stakeholder feedback.
Outcomes
Deliverable — a real client testimonial or reference, from presenting deliverables directly to the client, not the instructor.
What You’ll Learn
Turning four capstones into a polished portfolio site with real case studies; live system-design and debugging interview practice; behavioral interviewing; and rate-setting for local and international clients.
Outcomes
Deliverable — a live portfolio site, plus one submitted freelance proposal or job application.
What You’ll Learn
The launch month: a final capstone combining the student's lane specialization with the full skill stack, built and presented as if pitching a real employer — then a public showcase with an external industry panel.
Outcomes
Final Defense — graduation, certification, and a concrete next step: join Special Technician, freelance, placement with a partner company, or move to a deeper ML Engineer track.
Quarter Outcome
A graduate with a real client reference, a real portfolio, interview and negotiation readiness, and a concrete next step — not a certificate and a "good luck."
Step-by-Step Path
Follow a proven sequence designed for real results.
Build Real Projects
Apply your skills with hands-on projects every quarter.
Expert Guidance
Learn from industry experts and community support.
Career-Ready
Graduate with production experience and confidence.
AI-Lab System
Powered by our own classroom technology
Every class runs through Aisha Cahn College’s own AI-Lab platform — attendance, assignments, and grading, all automatic.
ai-lab.system — capability statusLive
Schedule
Monday – Saturday · 8:30 AM – 2:00 PM
Assessment
1 Quiz per Week1 Assignment per WeekCompulsory Capstone Project
Capabilities
8 automated
tap to expand
Meet Your Instructor
Learn from the Experts
Learn from people who do the work, not just teach it. Gain practical insights, real-world guidance, and knowledge shaped by industry experience.
Mr. Muhammad Musa Kaleem
AI Instructor · Senior Backend & MLOps Engineer
Experience
Founding Engineer, AI-Lab System
Aisha Cahn Foundation · 2025
Computer Vision Engineer
Leia Robots · Jan 2026 – Present
Senior Backend Developer
Gigmasters, Lahore · Feb 2024 – Jun 2025
Machine Learning Engineer
Wind to Energy, Rostock · Apr 2019 – Dec 2023
Education
MSCS
University of Rostock · 2019 – 2023
BSCS
University of Engineering & Technology (UET), Peshawar · 2012 – 2016
Student Work
Built by Our Students
Ready to start?
Apply for AI Engineering
Admissions open
New batch begins 30 September.
Seats in each batch are limited. Message us and we will reply the same working day — or come and see the campus first.
0Days
00Hours
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- CampusMuslim Hands Educational Complex, Wazirabad, Punjab
- Office hoursMonday to Saturday, 9:00 AM – 4:00 PM
- WhatsApp+92 300 1453445
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