SME India : Agentic AI Instructor

About Us

The name Interview Kickstart might have given you a clue. But here’s the 30-second elevator pitch - Interviews can be challenging. And when it comes to the top tech companies like Google, Apple, Facebook, Netflix, etc., they can be downright brutal. Most candidates don’t make it simply because they don’t prepare well enough. Interview Kickstart (IK) helps candidates nail the most challenging tech interviews.

To keep up with the upcoming trends, we are launching our new Agentic AI course.

Requirements

Technical Expertise in at least one of the following topics:

1. Fundamentals of Agentic AI

• Key AI Agent Frameworks: AutoGen, LangChain, CrewAI, LangGraph
• Understanding Multi-Agent Systems: Reflection, Planning, and Task Automation
• Introduction to ReAct (Reasoning + Action) Framework

● Prompt Engineering & Function Calling

2. Building a simple AI Agent (Code-based for SWEs / Low Code for other Tech Domains)

• Develop a modular, AI agent using LangGraph or CrewAI capable of reasoning, decision-making, and tool usage
• Understand the role of graph-based agent workflows (LangGraph) and multi-agent collaboration (CrewAI)
• Deploy an interactive AI assistant that can execute tasks autonomously

3. Building Applications with LLMs & Agents (Advanced)

● AI Agent Memory & Long-Term Context

• Multi-Agent Collaboration & Orchestration
• Deployment (LLMOps, Langchain, LlamaIndex etc.)
• Emerging trends: LLMOps, guardrails, and multi-agent systems

4. Evaluation & Optimizing AI Agents: Performance & Cost Efficiency

• AI Agent Performance Monitoring & Logging
• Optimizing Inference Speed & Model Costs
• Fine-Tuning vs. Prompt Engineering Trade-offs
• Evaluating Agent Effectiveness with Human Feedback

5. Designing Robust and Scalable AI Systems for Modern Applications (For SWEs)

• Introduction to AI system design: scalability, reliability, performance, and cost optimization.
• Common design patterns for AI applications: pipeline, event-driven, and microservices.
• System architecture for LLM applications: inference engine, data pipeline, API layer, and frontend integration.
• AI-specific challenges: managing large datasets, optimizing latency, and handling model updates.
• Advanced topics: LLMOps, multi-model orchestration, and AI system security.
• Evaluating AI systems: throughput, reliability, accuracy, and cost-efficiency.

6. Building Advanced Agents (Code-based for SWEs) Build an Advanced Horizontal Multi-Agent System for a use cases like: ● A multi-agent system that automates DevOps workflows, including CI/CD monitoring, infrastructure scaling, and system health diagnostics.

• A multi-agent AI healthcare assistant to automate medical FAQs, appointment scheduling, and patient history retrieval.
• An agentic system that analyzes application security risks, detects vulnerabilities, and suggests fixes.
• An AI-powered multi-agent system that analyzes, summarizes, and extracts key insights from legal documents.
• An AI-driven multi-agent system for supply chain automation, handling inventory management, demand forecasting, and logistics tracking.

7. Agentic AI For PMs Build Agentic AI system using low code / no code tools for use cases relevant to PMs such as:

• AI-Powered Feature Prioritization Tool
• Customer Sentiment Analysis & Roadmap Alignment
• AI-Driven Competitive Landscape Analysis

8. Agentic AI For TPMs Build Agentic AI system using low code / no code tools for use cases relevant to TPMs such as:

● AI-Powered Stakeholder Management Bot

• Multi-Agent AI System for Program Risk Management
• AI-Driven Engineering Capacity & Resource Allocation Agent

9. Agentic AI For EMs Build Agentic AI system using low code / no code tools for use cases relevant to TPMs such as:

• Multi-Agent System for Engineering Productivity & Burnout Monitoring
• Multi-Agent AI System for Engineering Roadmap & Strategy Planning
• AI Agent for Cloud Cost Optimization in Engineering Workloads

Preferred Qualifications:

• Prior experience building and deploying LLM or agent-based applications in real-world settings
• Strong proficiency with agent frameworks like LangGraph, CrewAI, or LangChain (code-based for SWEs and low-code or no-code for Tech Domains)
• Strong understanding of system design principles, especially in AI/ML-based architectures
• Demonstrated ability to explain complex technical topics to diverse audiences
• Experience teaching, mentoring, or creating content for working professionals in tech
• Excellent communication and collaboration skills, with a learner-first mindset
• Bonus: Contributions to open-source AI projects, publications, or prior experience with AI upskilling programs

Responsibilities:

● Instruction Delivery: Conduct lectures, workshops, and interactive sessions to teach machine learning principles, algorithms, and methodologies. Instructors may use various teaching methods, including lectures, demonstrations, hands-on exercises, and group discussions.

● Industry Engagement: Staying current with the latest trends and advancements in machine learning and related fields, engaging with industry professionals, and collaborating on projects or internships to provide students with real-world experiences.

● Research and Development: Conducting research in machine learning and contributing to developing new techniques, models, or applications.

• Constantly improve the session flow and delivery by working with other instructors, subject matter experts, and the IK team. ● Help the IK team in onboarding and training other instructors and coaches
• Have regular discussions with IK’s curriculum team in evolving the curriculum.
• Should be willing to work on weekends/evenings and be available as per the Pacific time zone