Instructor of AI, ML and DL

Role Summary

We are seeking a highly skilled and experienced AI, ML and DL Instructor to lead our hands-on vocational training program. The instructor will be responsible for teaching the practical application of Artificial Intelligence (AI) through core Machine Learning (ML) techniques and advanced Deep Learning (DL) architectures. The focus is on equipping students with job-ready skills to design, train, and deploy real-world AI solutions.

Key Responsibilities

NAVTTC Course Development & Delivery:

• Instruct on the architecture and application of modern LLMs (e.g., Transformer models like BERT, GPT, Llama, etc.) and related frameworks like Hugging Face.
• Prompt Engineering: Teach best practices for constructing effective prompts, few-shot learning, and advanced retrieval techniques (like Retrieval-Augmented Generation - RAG) for specific use cases.
• Model Specialization: Provide hands-on training in techniques for customizing LLMs, including Fine-Tuning and Parameter-Efficient Fine-Tuning (PEFT) on domain-specific datasets.
• Deployment & MLOps: Guide students through the entire LLM lifecycle, from data preparation (tokenization, embeddings) to deployment and integration into web applications using frameworks like Flask/Streamlit.
• Ethical AI: Cover critical topics on LLM ethics, including addressing bias, mitigating hallucination, and ensuring model safety and responsible usage.

A. Deep Learning (DL) & NLP Fundamentals

• Transformer Architecture: Deep conceptual understanding of Attention Mechanisms, Self-Attention, and the difference between Encoder-only, Decoder-only, and Encoder-Decoder architectures.
• Core NLP: Proficiency in Tokenization (subword tokenization), Embeddings, and common NLP tasks (Classification, Summarization, Text Generation).
• Frameworks: Expertise in PyTorch and/or TensorFlow and primary use of the Hugging Face ecosystem.

B. Large Language Model (LLM) Specifics

• Prompt Engineering: Mastery of zero-shot, one-shot, and few-shot prompting, as well as techniques like Chain-of-Thought (CoT) prompting.
• Fine-Tuning: Practical experience with supervised fine-tuning, Low-Rank Adaptation (LoRA), and understanding the training phases (Pre-training vs. Fine-tuning).
• Deployment & Scaling: Experience with deploying LLMs, managing hardware constraints (e.g., using quantization/GPGPU), and integrating models into production systems.

C. Artificial Intelligence (AI) Context

• RAG Implementation: Ability to design and implement Retrieval-Augmented Generation (RAG) pipelines to ground LLM responses in external, real-time data.
• Data Curation: Skills in curating, cleaning, and preparing massive, high-quality text datasets for training and fine-tuning.
• Ethical AI: Knowledge of model safety, fairness, and bias mitigation strategies specific to large-scale generative models.

Qualifications and Experience

Bachelor’s degree in Computer Science, Data Science, or a closely related field. Master's degree with a specialization in NLP or AI is highly preferred.

Minimum 1 year of formal teaching, corporate training, or vocational instruction experience, demonstrating the ability to convey complex LLM concepts clearly.

Certification Plus in NLP, Deep Learning, or Cloud ML platforms (e.g., Google Cloud/AWS/Azure AI certification).

Job Types: Full-time, Contract
Contract length: 12 months

Pay: Rs62,000.00 - Rs80,000.00 per month

Work Location: In person