Lead Teacher – Data & AI (IT SkillSprint)

IT SkillSprint 2027 is an intensive training programme for third-year university students, delivered through a collaboration between Ironhack, the Innovation and Digital Development Agency (IDDA), and the Ministry of Science and Education of the Republic of Azerbaijan.

About the Role

We are looking for an experienced, dynamic, and passionate Lead Teacher to deliver the Data & AI track of the IT SkillSprint programme.

As Lead Teacher, you will inspire, educate, and support third-year university students, helping them develop the data science (and analytics), machine learning, and applied AI engineering skills needed to succeed in a rapidly evolving technology landscape.

You will be responsible for delivering an outstanding academic experience, guiding students through hands-on projects, and ensuring that they achieve the programme’s learning outcomes.

Key Responsibilities

Academic Delivery & Instruction

• Deliver live lectures and lead interactive demonstrations and code-alongs, working with real-world datasets, notebooks, and end-to-end data and ML pipelines.

• Teach two hours of live theory and demonstrations daily, followed by two hours of guided lab and practice sessions.

• Prepare balanced daily lesson plans tailored to students’ learning needs, adjusting the pace and materials according to student progress.

• Support curriculum delivery based on the syllabus designed by Ironhack’s global education team, covering Python, mathematical foundations, data processing, machine learning, deep learning, MLOps, LLMs, RAG, and AI agents.

Student Mentorship & Guidance

• Guide student teams through major collaborative projects, including the Mid-Bootcamp Weather Intelligence Project and the Final Capstone.

• Support students in applying AI tools responsibly and effectively within real data analysis, machine learning, and AI engineering workflows.

• Administer assessments, review student submissions, and provide timely, accurate, and constructive technical feedback.

• Lead Q&A sessions and support students across a range of technical skill levels.

Classroom & Community Leadership

• Coordinate daily with the Teaching Assistant (TA) on lab execution and student progress tracking.

• Collaborate with Ironhack Programme Managers, Project Managers, and client representatives to ensure students achieve their academic and professional goals.

• Foster an energetic, supportive, and inclusive learning community that welcomes different learning styles and needs.

• Encourage critical thinking, collaboration, and continuous improvement throughout the programme.

Technical & Teaching Requirements

Experience

• 3+ years of professional experience in data science (and analytics), machine learning, or AI engineering.

• Previous teaching or mentoring experience in data / AI bootcamps, university programmes, or similar technical training environments.

• Hands-on experience building and deploying ML models and LLM-based applications (RAG, AI agents) is highly valued.

Core Technical Stack

• Python & Data: production-quality Python (structure, testing, reproducibility), Jupyter, NumPy, pandas, Polars, DuckDB, SQL, columnar data formats, ETL workflows and data pipelines, and data visualisation (Matplotlib).

• Mathematical Foundations: linear algebra (matrix operations, SVD/PCA), statistics and hypothesis testing, and numerical methods (optimisation, gradient descent, numerical stability).

• Machine Learning & Deep Learning: scikit-learn, XGBoost/LightGBM, model evaluation and validation (cross-validation, leakage prevention, metrics), and neural networks (MLP, CNN, RNN, transfer learning) with PyTorch and/or TensorFlow.

• LLMs, RAG & AI Agents: transformer architecture, tokenisation, fine-tuning and inference (e.g. vLLM), embeddings and vector databases (e.g. Milvus), OpenAI/Google APIs, tool calling, agent orchestration (e.g. Google ADK), and guardrails.

• MLOps & Tooling: Git/GitHub workflows, FastAPI model serving, Docker, CI/CD pipelines (GitHub Actions), monitoring, and cloud platforms (AWS, Azure, or Google Cloud; Google Colab).

AI Integration

• Practical experience with AI assistants such as ChatGPT, Claude, Gemini, Codex, or Cursor.

• Ability to teach students how to use AI for code review, debugging, documentation, and experiment notes, following a staged approach from limited use in early weeks to responsible, fully understood use in MLOps, LLM/RAG, and Capstone work.

• Strong understanding of responsible AI use, including data privacy, bias, explainability, and the citation and critical validation of AI-generated output.

Language & Communication

• Fluent English (C1 or above); all lectures and instructional materials will be delivered exclusively in English.

• Strong communication skills, with the ability to explain complex statistical, machine learning, and AI concepts clearly to both technical and non-technical audiences.

Programme Timeline

• January: Content review, preparation, and adaptation of learning materials.

• 15 February: Classes begin.

• April: Mid bootcamp project.

• 9 July: Programme ends.

Position Logistics & Terms

• Position Logistics & Terms

  • Group: Morning group.

  • Schedule: Part-time, 20 hours per week, Monday to Friday, 09:00 to 13:30 Baku time (GMT+4).

  • Format: Remote teaching, with three on-site trips to Baku, Azerbaijan: Opening Ceremony, Mid-Bootcamp Project, and Final Projects/Graduation.

  • Agreement: Freelance contractor agreement

  • Travel Benefits: Round-trip flights, local accommodation, and a daily per diem will be fully covered during the on-site periods in Baku.

  • Future Potential: Opportunity for involvement in future editions of the programme.