We are seeking a highly skilled AI & Machine Learning Engineer to design, develop, and deploy intelligent AI/ML solutions for our EdTech platform. The ideal candidate must have a Master’s degree in Artificial Intelligence, Machine Learning, or a related field and expertise in the latest AI methodologies, including Retrieval-Augmented Generation (RAG), Generative AI, Large Language Models (LLMs), and Multimodal AI. The role involves building scalable models, working with large datasets, and integrating AI capabilities into real-world applications.
Design and develop scalable AI/ML models for NLP, Computer Vision, Predictive Analytics, and Recommendation Engines tailored to the EdTech domain.
Fine-tune Large Language Models (LLMs) using LoRA, QLoRA, and PEFT methods for domain-specific use cases.
Build Retrieval-Augmented Generation (RAG) pipelines using vector databases such as Pinecone, Weaviate, or Faiss.
Implement prompt engineering strategies for intelligent content delivery, doubt resolution, and personalized assessments.
Integrate third-party and open-source AI models (e.g., OpenAI GPT-4, Claude, Gemini, Llama) into the learning platform.
Leverage LangChain, Hugging Face Transformers, and modern ML frameworks (TensorFlow, PyTorch, Scikit-learn, Keras) for development.
Work with real-time and batch data pipelines using tools like Apache Spark, Kafka, Databricks, and Hadoop.
Deploy AI models to production using cloud platforms (AWS SageMaker, Google Vertex AI, Azure ML) and tools like Docker and Kubernetes.
Set up and manage CI/CD pipelines and adopt MLOps best practices for automated deployment, monitoring (MLflow, Weights & Biases), and retraining.
Optimize inference for cloud/edge deployment and implement model explainability (e.g., SHAP, LIME).
Collaborate with cross-functional teams including instructional designers, data engineers, and full-stack developers.
Continuously explore and experiment with the latest advancements in AI for education, ensuring ethical, explainable, and equitable models.
Document experiments, model architectures, performance benchmarks, and deployment processes for reproducibility and audit readiness.
Education: Master’s degree in AI, Machine Learning, Data Science, Computer Science, or a related field (mandatory).
Experience: 5+ years in AI/ML engineering roles, preferably in EdTech or learning analytics domains.
Languages & Libraries: Expert in Python (NumPy, Pandas, Scikit-learn). Knowledge of R or Java is a plus.
Frameworks: Strong experience with TensorFlow, PyTorch, Keras, Hugging Face Transformers, and LangChain.
LLMs & RAG: Hands-on with LLM fine-tuning, prompt engineering, RAG pipelines, and vector search engines.
Cloud & Big Data: Solid experience with AWS, GCP, Azure, and platforms like Databricks, Kafka, Spark, Hadoop.
MLOps Tools: Familiar with Docker, Kubernetes, MLflow, Weights & Biases, and CI/CD practices.
Visualization/Prototyping: Exposure to FastAPI, Flask, Gradio, or Streamlit is a bonus.
Math & Stats: Strong understanding of statistics, probability, linear algebra, and deep learning fundamentals.
Soft Skills: Excellent problem-solving, analytical, and communication skills. Ability to mentor junior team members is an advantage.