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 rapid, 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