Theseus AI Lab

Cohort 2

Cohort 1

Most professionals think AI started with ChatGPT. It didn't.This free resource walks you through the full evolution — from deterministic software and statistical models, through classical ML and deep learning, all the way to Generative and Agentic AI.You'll build a clear mental model of how each era connects, what problems each solves, and why older techniques still matter today.No math. No jargon. Just the clarity you need to think and talk about AI with confidence.

AI Landscape: From Statistical Methods to Agentic AI 

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Designing scalable, reliable software systems — architecture patterns, distributed systems, and technical tradeoffs.

By the end of this course, you’ll transition from a software engineer to a confident AI engineer—able to identify the right problems, design

AI Engineer & Consultant for F-500 firms

Sujeet Mishra

AI Engineer | Entrepreneur | AI Consultant & Trainer for Fortune-500

AI/ML | Search | Ex-Microsoft, Yahoo

Prasenjit Mukherjee

Adjunct Faculty | Quantum ML & Big Data | Ex-Microsoft, Yahoo | IIT Madras

AI Engineering for Software Engineers

Software Development Engineer

Qualcomm

Wipro

Apple

Microsoft

Yahoo

For software engineers, moving into AI engineering is not just about learning new tools—it’s a mindset shift.Over the past year, we’ve worked with engineering teams, from Fortune 500 companies to unicorn startups, helping them adopt AI-native and agent-based systems. One pattern shows up repeatedly: engineers don’t struggle because they can’t build, but because AI systems behave very differently from deterministic software.⚙️ Engineers must learn how to design, evaluate, and operate probabilistic systems, reason about failures and trade-offs, and think end-to-end—from use-case identification to production and scale.Many engineers hesitate because they believe AI engineering requires deep ML or statistics expertise. In reality, modern AI engineering is about system design, evaluation, observability, and production readiness—skills engineers already have but need to apply in a new context.🚀 This course provides a practical 6-week roadmap to make that transition by building and extending a real, production-grade agentic system.By the end, you won’t just understand AI systems—you’ll think and operate like an AI engineer.For queries, write to us: ai-education@theseuslab.ai

A 6-week roadmap for software engineers to become production-ready AI engineers

Use LLMs to 10x day-to-day engineering productivity

Design and build agentic systems using LangChain/LangGraph

Build reliable agentic systems by designing for evaluation and observability

Take AI ideas from use-case discovery to production and scale

Apply everything you learn to a real, end-to-end capstone project

Basic understanding of SDLC concepts like databases, APIs, frontend/backend systems, and servers

Familiarity with Software Engineering Fundamentals

Some programming experience, even if not recent—or willingness to spend ~8 hours pre-course to learn Python (we will provide resources)

Prior Programming Exposure (or Willingness to Ramp Up)

Openness to learning, experimenting, and thinking differently

Curiosity and Willingness to Experiment

Software engineers looking to transition into AI engineering roles and build production-grade AI systems

AI engineers and data science professionals who want to get a hands-on experience of building, evaluating and scaling agentic AI applications

Product managers and Tech leaders seeking a practical, system-level view of how production-ready AI applications are designed and shipped

Learn directly from Theseus AI Lab

Theseus AI Lab

Courses

AI Engineering for Software Engineers

Lightning Lessons

AI Engineering 101 for Software Engineers

Resources

AI Landscape: From Statistical Methods to Agentic AI