Senior ML Engineer

Key Responsibilities

• Work closely with Product, Design, Content, and Engineering to understand student problems deeply and ship AI features end-to-end into the product.
• Do hands-on research + experimentation to find the best approach–model selection, prompting, evaluation, iteration, and rapid learning.
• Build and improve AI systems for real learning workflows like numerical problem solving, image-to-text extraction, and AI-powered content/lecture generation.
• Debug, optimize, and improve performance (accuracy, latency, cost), and maintain strong evaluation + testing pipelines (test sets, automated checks, regression testing) to ensure reliability at scale.
• Own outcomes like a builder— you don’t watch the clock, you watch the impact: driven to do bold, innovative work that students have never experienced before.

Key Requirements

• 3–4 years of hands-on experience building and deploying AI solutions, with strong practical work in LLMs/GenAI (prompting, tool/function calling, RAG or similar approaches, and evaluation).
• Strong Python + production engineering: building scalable APIs/services using FastAPI/Django/Flask, and solid experience with ML stacks like PyTorch/TensorFlow/Scikit-learn/Keras + Hugging Face and supporting pipelines/scripts.Deep ML lifecycle + multimodal exposure: strong command over prompt → evaluate → iterate → improve, with focus on accuracy/robustness; experience across text, image, audio, video (tools like ElevenLabs/Wav2Lip/Manim/AI4Bharat are a plus).
  • Builder mindset + collaboration: sharp problem-solving in ambiguous situations, strong experimentation skills, and clear communication to align technical decisions with product outcomes.