Machine Learning Software Development Instructor

The NxtWave Institute of Advanced Technologies (NIAT) offers NxtWave's flagship four-year, on-campus Computer Science program. It features one of India's most advanced, industry-aligned technology curricula. Located at the heart of Hyderabad's technology ecosystem, NIAT's new-age campus is surrounded by global organizations such as Google, Microsoft, Apple, Infosys, and TCS - providing students with meaningful exposure to the professional technology landscape. At NIAT, accomplished software engineers and technology professionals mentor students and help them develop practical industry capabilities. Its curriculum combines academic foundations, hands-on learning, and real-world technology requirements.

Key Responsibilities

• Deliver engaging, in-person classroom sessions on machine learning, deep learning, data science fundamentals, and MLOps.
• Explain foundational and advanced topics such as supervised, unsupervised, and reinforcement learning, statistical foundations, and the end-to-end machine learning lifecycle.
• Conduct hands-on laboratory sessions involving data preprocessing, feature engineering, model training, hyperparameter tuning, and evaluation.
• Help students build ML-powered applications using Python, popular ML/DL frameworks, and model-serving APIs.
• Introduce students to MLOps practices through model deployment, versioning, monitoring, and automated training pipelines.
• Design industry-aligned lesson plans, laboratory exercises, assignments, assessments, projects, and instructional resources.
• Teach essential algorithms and architectures, including linear and logistic regression, decision trees, ensemble methods, clustering, CNNs, RNNs, and transformers.
• Provide practical exposure to NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Jupyter Notebooks, and cloud-based ML platforms.
• Understand individual learning needs and provide students with personalized academic support. Resolve technical questions and learning challenges promptly while providing continuous mentorship.
• Guide students through ML projects, case studies, programming assignments, and model-debugging exercises. Review code, model pipelines, laboratory exercises, and project submissions for accuracy, quality, performance, and best practices.
• Contribute to curriculum development based on student feedback, industry requirements, and emerging ML technologies.
• Encourage curiosity, experimentation, critical thinking, collaboration, and independent problem-solving.
• Effectively manage and engage a classroom of approximately 70-100 students.
• Stay current with machine learning, deep learning, generative AI, MLOps, and modern teaching methodologies. NxtWave Institute of Advanced Technologies

Requirements:

• A bachelor's or master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, or a related discipline.
• Strong academic or professional understanding of machine learning, deep learning, and related technologies.
• Previous experience in teaching, technical training, mentoring, or academic instruction will be an advantage.
• Practical experience in building, training, and deploying machine learning models or ML software development is preferred.
• Strong passion for teaching, mentoring, and supporting student success.
• Ability to communicate technically complex and abstract concepts in a simple, structured, and engaging manner.
• Willingness to deliver in-person classroom instruction and conduct hands-on laboratory sessions. Strong alignment with NxtWave's vision, values, and learner-focused culture.

Skills

Must-Have:

• Strong understanding of supervised, unsupervised, and reinforcement learning paradigms.
• Strong knowledge of regression, classification, clustering, dimensionality reduction, and ensemble methods.
• Sound understanding of neural networks, backpropagation, CNNs, RNNs, LSTMs, and transformer architectures.
• Practical ability to preprocess data, engineer features, train models, and evaluate performance using appropriate metrics.
• Proficiency with Scikit-learn, TensorFlow, PyTorch, or Keras, or equivalent frameworks.
• Strong working knowledge of Python and commonly used data-science libraries such as NumPy, Pandas, and Matplotlib.

Good to Have

• Familiarity with Git and version-control systems.
• Knowledge of deep learning specializations such as computer vision, natural language processing, or generative AI.
• Understanding of large language models, prompt engineering, and retrieval-augmented generation.
• Familiarity with MLOps tools such as MLflow, Kubeflow, Airflow, or Docker.
• Knowledge of cloud ML platforms such as AWS SageMaker, Google Vertex AI, or Microsoft Azure ML.
• Exposure to big-data tools such as Spark, Hadoop, or distributed-training frameworks.
• Understanding of statistics, probability, and linear algebra as applied to machine learning.
• Familiarity with data-visualization tools such as Tableau, Power BI, or Seaborn.
• Knowledge of model explainability, fairness, and responsible AI practices.
• Relevant certifications such as TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, or equivalent credentials.
• Awareness of current industry standards, emerging technologies, and ML best practices.
• Ability to adapt teaching approaches to different learning styles and academic requirements.
• Openness to constructive feedback and continuous professional development.
• A strong sense of ownership, initiative, accountability, and commitment to teaching excellence.