Senior Machine Learning Engineer

Job Description Company Description FastutorAI is an innovative EdTech platform transforming education through advanced artificial intelligence. We develop personalized, data-driven learning experiences that help learners progress faster and more effectively. Our mission is to make education accessible and adaptive by combining state-of-the-art machine learning with a human-centered approach. At FastutorAI, we are committed to building intelligent systems that shape the future of learning. Role Description FastutorAI is seeking a highly experienced Senior Machine Learning Engineer to lead the design, development, and deployment of large-scale machine learning systems. This senior role requires deep technical expertise in model architecture, production-grade ML pipelines, optimization, and scalable infrastructure. You will be responsible for end-to-end ML lifecycle managementfrom data ingestion and feature engineering to model training, deployment, monitoring, and continuous improvement. You will also contribute to long-term AI strategy, mentor junior engineers, and collaborate with cross-functional teams to deliver high-impact AI solutions across the platform. Key Responsibilities Model Architecture & Development Architect, build, and optimize advanced ML and deep learning models for personalization, recommendation, content generation, and adaptive learning experiences. Lead experimentation, model evaluation, hyperparameter tuning, and architecture design for large-scale machine learning systems. Implement robust feature engineering pipelines for structured and unstructured educational data. Production ML Engineering Design and maintain scalable training, testing, and deployment pipelines using modern MLOps frameworks. Implement model monitoring, drift detection, logging, and performance optimization. Ensure reliability, latency performance, and cost-efficiency of production models. Data Engineering & Infrastructure Collaborate with data engineering to build datasets, ETL pipelines, and ingestion strategies for training and inference. Work with cloud platforms (AWS, GCP, or Azure) to deploy and manage ML services in production. Integrate ML systems with FastutorAI backend services, APIs, and real-time data streams. Cross-Functional Leadership Collaborate with product, engineering, and content teams to align ML solutions with user needs and platform goals. Mentor and guide junior machine learning engineers and data scientists. Contribute to roadmap planning, technical architecture decisions, and best-practice standards across the AI organization. Qualifications Required 5+ years of hands-on experience building, deploying, and maintaining machine learning systems in production. Expert proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Strong experience with deep learning architectures, transformers, embeddings, and large-scale modeling. Proficiency with MLOps tools, model versioning, CI/CD pipelines, and automated workflows. Solid command of data structures, algorithms, distributed systems, and software engineering best practices. Strong knowledge of cloud platforms (AWS, GCP, or Azure) for production ML environments. Experience deploying and scaling real-time or batch ML systems. Ability to translate ML concepts into actionable engineering decisions. Preferred Master's or PhD in Computer Science, Machine Learning, Data Science, Mathematics, or a related technical field. Experience with LLM fine-tuning, retrieval-augmented generation (RAG), or multimodal models. Experience in EdTech or personalized learning systems. Familiarity with vector databases, reinforcement learning, or distributed training. What FastutorAI Offers Opportunity to build foundational AI systems that directly impact learners and educators. A highly collaborative environment focused on innovation and technical excellence. Autonomy to shape machine learning strategy and influence product direction. Remote-friendly culture with flexible work structure. Professional growth in advanced AI engineering, research, and large-scale systems design.