Machine Learning Lead

Job Overview:As a Technical Lead, you will be responsible for leading the design, development, and deployment of AI-powered Edtech solutions. You will mentor a team of engineers, collaborate with data scientists, and work closely with product managers to build scalable and efficient AI systems. The ideal candidate should have 8-10 years of experience in software development, machine learning, AI use case development and product creation along  with strong expertise in cloud-based architectures.Key Responsibilities:AI Tutor & Simulation IntelligenceArchitect the AI intelligence layer that drives contextual tutoring, retrieval-based reasoning, and fact-grounded explanations.Build RAG (retrieval-augmented generation) pipelines and integrate verified academic datasets from textbooks and internal course notes.Connect the AI Tutor with the Simulation Lab, enabling dynamic feedback — the system should read experiment results, interpret them, and explain why outcomes occur.Ensure AI responses remain transparent, syllabus-aligned, and pedagogically accurate.Platform & System ArchitectureLead the development of a modular, full-stack platform unifying courses, explainers, AI chat, and simulation windows in a single environment.Design microservice architectures with API bridges across content systems, AI inference, user data, and analytics.Drive performance, scalability, and platform stability — every millisecond and every click should feel seamless.Reliability, Security & AnalyticsEstablish system observability and monitoring pipelines (usage, engagement, AI accuracy).Build frameworks for ethical AI, ensuring transparency, privacy, and student safety.Set up real-time learning analytics to measure comprehension and identify concept gaps.Leadership & CollaborationMentor and elevate engineers across backend, ML, and front-end teams.Collaborate with the academic and product teams to translate physics pedagogy into engineering precision.Evaluate and integrate emerging tools — multi-modal AI, agent frameworks, explainable AI — into the product roadmap.Qualifications & Skills:8–10 years of experience in software engineering, ML systems, or scalable AI product builds.Proven success leading cross-functional AI/ML and full-stack teams through 0→1 and scale-up phases.Expertise in cloud architecture (AWS/GCP/Azure) and containerization (Docker, Kubernetes).Experience designing microservices and API ecosystems for high-concurrency platforms.Strong knowledge of LLM fine-tuning, RAG pipelines, and vector databases (Pinecone, Weaviate, etc.).Demonstrated ability to work with educational data, content pipelines, and real-time systems.Bonus Skills (Nice to Have):Experience with multi-modal AI models (text, image, audio, video).Knowledge of AI safety, ethical AI, and explain ability techniques.Prior work in AI-powered automation tools or AI-driven SaaS products.