AI Engineering Lab — Kalis Technology Services

The Programme

What is the AI Engineering Lab?

Core Philosophy

Not another passive course. A selective, production-grade builder lab.

Most AI courses teach you how to copy-paste API wrappers in a notebook. This lab is different.

You will design, architect, and deploy a hardened, production-ready Agentic AI application from scratch over 12 intensive weeks.

Architect Guidance

Guided by an Architect

You aren't left alone with video recordings. Build under the direct guidance of a Systems Architect who knows exactly what it takes to design, build and deploy resilient AI systems into production.

Structure

12-Week Selective Cohort

Skip the isolation of self-study. Whether you are a graduate launching your career, a student looking for industry edge, or a professional engineering an AI pivot, you will work alongside a driven, like-minded cohort, mirroring the daily workflow of modern engineering teams.

The Blueprint

Production-Grade Agentic Stack

While the exact application shifts with each cohort, you will master the industry-standard architecture required to ship resilient AI systems.

⚙️

Stateful Orchestration

LangGraph / CrewAI / Autogen

Master multi-agent runtimes using cyclical graphs, explicit state machines, routing loops, and human-in-the-loop interruption patterns.

🐍

Asynchronous Runtimes

Python AsyncIO / FastAPI

Build concurrent, non-blocking agent background workers capable of executing heavy parallel tool actions and stream handling smoothly.

🌲

Vector Databases

Pinecone / Qdrant / pgvector

Architect robust long-term semantic memory spaces leveraging dense vector embeddings, hybrid search, and metadata pre-filtering.

💾

State & Telemetry Layer

PostgreSQL / Prisma / Redis

Solve complex distributed systems challenges: manage token caching, prevent multi-agent race conditions, serialize graph states, and maintain immutable audit trails.

🛡️

Tracing & Evaluation

LangSmith / Phoenix / Arize

Trace complex multi-turn execution steps, establish real-world validation guardrails, and set up continuous accuracy evaluation pipelines.

☸️

Cloud-Native Deployment

Docker / Kubernetes

Containerize autonomous microservices and orchestrate deployment manifests for high-availability production environments.

The Roadmap

The Execution Pipeline

From foundational pre-work to a live production showcase—explore your 12-week engineering sprint.

Cohort Warm-Up • Self-Study

Phase 0: Architecture Pre-Work & Foundations

Get up to speed before the live cohort begins. Access our curated repository of recorded training videos covering core Python asynchronous patterns, Docker environments, and git workflow basics to ensure you hit the ground running on Day 1.

Weeks 01 – 03

Phase 1: Stateful Orchestration & Core Architecture

Initialize your development runtime environment and model the core multi-agent system state machine. You will design cyclical execution graphs, establish deterministic routing loops, manage asynchronous microservice skeletons, and map ingestion pipelines for unstructured context data.

Weeks 04 – 06

Phase 2: Semantic Memory & Distributed Runtimes

Wire up long-term memory systems by developing chunking and embedding logic. Integrate hybrid search spaces (vector databases paired with traditional relational querying), build parallel tool execution loops via Python AsyncIO, and implement persistence schemas to prevent state corruption during concurrent actions.

Weeks 07 – 09

Phase 3: Telemetry, System Guardrails & Evaluation

Move past basic correctness into defensive engineering. Hook up end-to-end tracing stacks to profile deep execution loops. Implement structural validation guardrails to handle non-deterministic JSON parsing faults, build prompt-injection firewalls, enforce role-level data constraints, and spin up automated evaluation suites against validation datasets.

Weeks 10 – 11

Phase 4: Latency Hardening & Containerization

Optimize your system for production scaling. Run rigorous load testing to measure and document p50, p95, and p99 system latency profiles. Gracefully handle token rate-limit thresholds, containerize your autonomous workers using Docker, and execute automated deployment routines onto live infrastructure.

Week 12 • The Finale

Phase 5: Live Production Demo Day

The program highlight. Showcase your fully autonomous, production-hardened Agentic AI application live to a selective panel of tech leaders, systems architects, and engineering peers. Demonstrate your system's edge-case recovery, audit trails, and performance graphs, and graduate with a production piece you fully own.

Target Cohort

Who Is This Lab Engineered For?

We accept a highly selective group of 12 builders per cohort across three specific profiles.

Ambitious Tech Graduates

You have the theoretical foundation and know how to write code, but you lack a high-signal production piece on your GitHub. You need to transition from academic logic to systems engineering to stand out to elite tech teams.

Pivoting Software Engineers

You are established in a traditional software engineering or data role but want to execute a definitive pivot into AI systems architecture. You don't need another introductory syntax certificate; you need verifiable, production-grade domain experience.

Pre-Final & Graduate Students

You want to break out of the standard academic curriculum loop. By mastering advanced orchestration and containerized infrastructure before graduating, you leapfrog standard entry-level competition entirely.

The Core Difference

Code Reviews, Not Classrooms

This isn't mentorship in the traditional sense—it's industrial-grade production quality control.

Throughout the 12 weeks, your system architecture, data models, and asynchronous loops will be scrutinized directly by a Senior Systems Architect with real-world enterprise experience. You will be held to the exact engineering standards demanded by elite development squads.

🔬

Production-Ready Code Reviews

We don't just check if your code runs. We review it for state serialization safety, resource cleanup, and execution stability.

📐

Architectural Stress Tests

Defend your system design choices. Learn to handle concurrent agent tasks without deadlocks or token budget corruption.

🛡️

Pre-Flight Security Gates

Your agent will not hit production deployment until its prompt injection vectors are closed and your runtime environment is locked down.

src/orchestrator/agent_loop.py (PR #04)

# Pull Request Review: Multi-Agent Pipeline Execution Loop
async def execute_agent_run(session_id, graph_state):
ctx = await load_session_state(session_id)
try:
result = await routing_engine(graph_state, ctx)

                                    📐
                                    Senior Systems Architect 
                                    reviewed 4 mins ago
                                

                                    "CRITICAL OVERLOOK: You are executing the routing engine tool actions sequentially here. On high load, this will trigger severe p99 latency spikes and model rate-limiting thresholds. Refactor this block to bundle independent agent sub-tasks via asyncio.gather() and introduce explicit retry back-offs."
                                
return post_process(result)
except Exception as e:
await rollback_state_serialization(session_id)

Pipeline

Selection & Onboarding Sequence

The 4-stage deployment path to secure your seat.

01

[01 / INIT]

Quick Application

Drop your name, email, phone number, and resume. No long cover letters or essays required—it takes less than 60 seconds.

02

[02 / REVIEW]

Architect Screening

The engineering team reviews your profile within 24 hours. Profiles are checked to guarantee baseline proficiency standards.

03

[03 / SYNC]

15-Min Alignment Call

A casual technical sync with our Architect to lock down your objectives, discuss expectations, and answer your queries.

04

[04 / DEPLOY]

Get onboard

Upon approval, clear your program fee to claim your dedicated workspace, and unlock Phase 0 modules.

FAQ

Program Specifications

Clear answers regarding commitment thresholds, operational structures, and technical prerequisites.

What is the baseline technical prerequisite to survive the cohort?

If you have a foundational handle on coding, you can absolutely succeed here. If you understand standard loops, conditional statements, and how data moves through functions—whether that’s in Python, JS/TS, Java, or C++—you have the baseline tools. We use Python for the core AI orchestration layer, but your prior programming familiarity combined with our pre-cohort training track is more than enough to get you up to speed smoothly.

How does the 14-hour weekly commitment break down?

This lab is structured intentionally to fit seamlessly alongside your full-time job or college schedule without disrupting your day-to-day commitments. It breaks down into just 2 hours per day across weekdays for your individual deep-work production engineering (writing code, modeling states at your own pace) and 4 hours on Saturday for our live, collaborative architectural reviews, alignment check-ins, and team code triage.

Am I allowed to use AI coding assistants (GitHub Copilot, Cursor, etc.) during the program?

Absolutely. Leveraging AI assistants, context-aware IDEs, and autonomous agents has become the modern baseline industry practice for high-velocity development. We encourage you to use the tools of your choice to speed up syntax generation. Our focus is not on testing your ability to manually memorize boilerplate code—it is on teaching you how to design complex architectures, handle system states, debug live telemetry faults, and orchestration loops that AI cannot build for you.

What happens if my code fails an architectural review?

Not passing a review on the first try is completely normal—it’s exactly how real production engineering teams iron out bugs. If your code reveals a race condition or a state issue, you won't be left to struggle alone. The architect will jump in to guide you, breaking down the systemic fault with clear feedback and working alongside you during team triage hours to help you confidently refactor and harden your application.

Do I get a certificate of completion at the end?

Yes, you will receive an official Certificate of Completion. However, while a certificate is a great milestone, we focus on delivering infinitely higher-signal proof of your skills: a live, fully containerized multi-agent system deployed on cloud infrastructure, an elite peer network of engineering minds, and an immutable audit trail of architect-reviewed code on your GitHub that proves you can build real systems.

Who owns the intellectual property of the application built?

System Status: Intake Active

Claim Your Production Repository

Cohort 1 is strictly capped at 12 active engineer seats to preserve individual architect code review bandwidth. Shift your focus from simple API wrappers to industrial-grade infrastructure.

⚙️

No Outdated Training Videos

Engage in active, live-engineered architectural sprints designed around immediate execution.

🔬

Production-Grade Code Quality

Learn to handle complex multi-agent state machines, concurrency, and real-world execution tracking.

💼

High-Signal Proof of Work

Build an immutable audit trail of containerized setups on cloud systems that top tech teams respect.

invoice_cohort_01.pdf

Standard Program Fee ₹25,000

Introductory Cohort Offer - ₹10,000

Total Investment

All-inclusive access

₹15,000

INR

Apply Online Now Chat with us on WhatsApp