KEY RESPONSIBILITIES
Build the agent architecture: specialist agents for explanation, memorization, and practice behind a single companion persona
Build the routing layer that knows the student's context and calls the right specialist at the right time
Build RAG over NCERT and curriculum content, tied to the concept hierarchy
Make the companion teach well in Hinglish — tone, empathy, and code-switching that sounds like a real teacher
Own evaluation: is the explanation correct, is it pitched right, did the student actually learn — automated and human-in-the-loop
Implement mastery scoring and the logic that decides what a student sees next
Own guardrails and safety for a student-facing AI, and keep latency and cost in check at scale
REQUIRED SKILLS
5+ years in applied ML or NLP
Strong Python (PyTorch / TensorFlow / scikit-learn)
Hands-on with LLMs: prompt engineering, RAG, embeddings, vector stores
Built multi-agent or tool-using systems in production, not just demos
Ability to design an eval suite,



not just run one
Cloud ML infrastructure (GCP Vertex AI preferred; SageMaker / Azure ML also fine)
Experience taking ML systems from prototype to production
NICE TO HAVE
Education or tutoring products — knowledge tracing, mastery models, spaced repetition
Indian language NLP and Hinglish code-switching
Production experience with Gemini / Claude / GPT-class models
Speech and voice AI (Whisper, Deepgram, TTS)
Serving LLMs cheaply at scale — caching, routing, smaller models where they suffice
HOW WE WORK
Small teams with real ownership — you shape how your area works, rather than inheriting a process
We ship quick, measure honestly, and cut what isn't working instead of defending it
Research and data sit next to product here; opinions lose to evidence
AI tooling is part of the workflow for everyone, not just the engineers

📌 Senior AI Engineer (Noida)
🏢 Infinity Learn
📍 Noida