Job Title: AI Course Developer – 3-Month Contract (Remote, Europe)

Location: Remote, Europe

Time Zone: Must be available to work EST hours

Duration: 3 Months Contract

Academian is seeking an AI Subject Matter Expert (SME) to develop comprehensive, engaging course content on AI fundamentals, LLM architectures, AI project planning, and Generative AI applications. This role is crucial in creating educational resources that will equip learners with the knowledge to navigate and innovate within the rapidly evolving AI landscape. If you're passionate about AI and have hands-on experience with various AI and GenAI technologies, we invite you to join our team for this remote opportunity.

Key Responsibilities:

AI Fundamentals and Course Content Development

• Course Introduction: Develop modules on the basics of AI, Generative AI, and Large Language Models (LLM) architectures.

• AI Techniques: Provide overviews of traditional AI approaches (ML, NLP, DL, RL) and contrast them with Generative AI techniques.

• AI & GenAI Selection: Create criteria for AI vs. GenAI use in business applications, including when to use ML vs. LLM models.

• LLM Architectures: Deliver content on Transformer, GAN, and other LLM architectures, including multimodal LLMs.

LLM Architecture and Selection

• LLM Analysis: Develop course material on different LLM architectures, such as Llama3 and Gemma 2, and tools like OpenAI & Anthropic Claude.

• Evaluation and Deployment: Provide instruction on model evaluation, token counting, deployment options, and considerations of model size, speed, and cost.

AI Project Planning and Implementation

• AI Project Scoping: Design courses on identifying and quantifying AI project value and translating business objectives into technical plans.

• Technology Selection: Teach methods for selecting appropriate technologies and models for specific business needs.

Vector Database Design Patterns

• Database Usage: Develop lessons on vector and graph databases for AI applications, with a focus on comparing VectorDB and GraphDB options.

• Database Selection: Explore various database platforms, such as Singlestore, MongoDB, and Milvus, for AI app integration.

LLM Alignment and Reinforcement Learning

• LLM Tuning: Teach concepts of LLM alignment, including RLHF and feedback models, along with instruction tuning for LLMs.

LLM Fine-Tuning and Efficiency Techniques

• Fine-Tuning Strategies: Design modules on fine-tuning using libraries like Hugging Face, with techniques such as PEFT, QLORA, and AdaLORA.

Prompt Engineering and RAG Architectures

• RAG Pipelines: Provide instruction on architecting production-grade RAG pipelines on cloud platforms (AWS, Azure, GCP).

• Advanced Architectures: Explain when to use RAG vs. fine-tuning in AI applications.

LLM Evaluation and Scaling

• Performance Evaluation: Teach metrics and best practices for LLM evaluation, including BLEU, ROUGE, and MMLU.

• Scaling and Deployment: Design modules on deploying and scaling LLMs on cloud platforms and optimizing inference with ML hardware accelerators.

Security, Compliance, and UX

• ML Guardrails: Develop content on implementing ML guardrails and compliance measures for LLM use, and collecting user feedback for continuous improvement.

LLMOps, Monitoring, and Lifecycle Management

• Operational Management: Provide instruction on MLOps, DevOps, and AIOps, including CI/CD, monitoring, and detecting model drift.

AI Agents and Task Management

• AI Agent Overview: Teach the basics of AI agents, Langgraph, and Autogen for multi-agent collaboration and task management.

AI Ethics and Regulatory Compliance

• Responsible AI: Design content on AI regulations and ethics, covering governance and responsible AI practices (e.g., EU AI Act).

Requirements:

• Demonstrated experience in developing AI courses, with a focus on Generative AI and LLMs.

• Strong familiarity with cloud platforms (AWS, Azure, GCP) and AI architecture.

• Expertise in vector databases, RAG architectures, and AI project planning.

• Ability to communicate complex AI concepts clearly and concisely to diverse audiences.

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