We are seeking an AI/ML Engineer with 4 to 7 years of experience in designing, developing, and deploying machine learning and generative AI solutions. The ideal candidate will have strong expertise in machine learning, deep learning, NLP, recommendation systems, and LLM-based applications, preferably within EdTech, Learning Management Systems (LMS), or digital learning environments.

Key Responsibilities :

- Design, develop, and deploy scalable AI/ML models for educational products and platforms.

- Build and optimize recommendation engines for personalized learning journeys.

- Develop NLP solutions for automated assessments, content tagging, question generation, and learner feedback analysis.

- Implement Generative AI and LLM-powered applications such as AI tutors, learning assistants, content generation, and chatbots.

- Fine-tune and evaluate foundation models using domain-specific educational datasets.

- Develop predictive analytics models for learner performance, engagement, retention, and skill-gap identification.

- Build end-to-end MLOps pipelines for model training, deployment, monitoring, and continuous improvement.

- Collaborate with Product, Engineering, Data Science, and Content teams to identify AI-driven opportunities.

- Ensure data security, privacy, fairness, and responsible AI practices.

- Monitor model performance and continuously improve accuracy, scalability, and reliability.

Required Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or related field.

- 4 to 7 years of hands-on experience in AI/ML development and deployment.

- Strong proficiency in Python and ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or Keras.

- Experience with NLP techniques, Large Language Models (LLMs), and Generative AI applications.

- Expertise in supervised, unsupervised, and deep learning algorithms.

- Experience with vector databases, embeddings, RAG (Retrieval-Augmented Generation), and prompt engineering.

- Proficiency in SQL and working with large-scale datasets.

- Experience deploying models on AWS, Azure, or Google Cloud platforms.

- Strong understanding of MLOps tools such as MLflow, Kubeflow, Airflow, Docker, and Kubernetes.

- Knowledge of API development and integration using FastAPI, Flask, or similar frameworks.