Senior AI Engineer – GenAI Systems & AI Agent Architecture

Full Description

📌 Job Title: Senior AI Engineer – GenAI Systems & AI Agent Architecture

📍 Location: Noida / Hybrid

🕒 Type: Full-time

💰 Compensation: Competitive – based on experience (CTC + ESOP options)

🚀 About YoLearn.ai

YoLearn.ai is building the world’s most emotionally intelligent, personalized learning OS — powered by AI tutors, coaches, and study buddies.

Our platform blends deep pedagogical thinking with cutting-edge GenAI, LLMs, and agentic architectures to transform how students learn and how teachers teach.

We're looking for a Senior AI Engineer with a strong foundation in machine learning, LLMs, and AI agent infrastructure — who has built real AI apps, not just played with notebooks. You’ll work closely with the founder, AI research engineers, backend devs, and product teams to power everything from tutoring agents to live multimodal avatars.

🎯 Responsibilities🧠 AI & ML System Development

• Architect, train, fine-tune, and deploy models (ML + DL + LLMs)

• Build and scale GenAI applications: RAG systems, recommender engines, forecasting models

• Engineer robust AI agents with memory, personalization, tool access, and reasoning logic

• Handle multi-agent orchestration, streaming audio/video interfaces, and real-time AI flows

🧰 Engineering & Infra

• Design and optimize data pipelines: cleaning, preprocessing, feature engineering

• Build production APIs (FastAPI preferred) for AI tools and core platform functionality (billing, notifications, usage logs, profiles, history)

• Manage CI/CD pipelines, containers (Docker), orchestration (Kubernetes), and deployment workflows

• Integrate with AWS services: Sagemaker, Bedrock, Lambda, EC2, ECS/EKS, Redis, S3, Athena, Step Functions, etc.

• Work on LLM fine-tuning, retrieval augmentation, and model context protocols (MCP)

🤖 GenAI & LangChain Ecosystem

• Build GenAI tools using OpenAI, LLaMA, DeepSeek, Mistral, etc.

• Use LangChain, LLM orchestration frameworks, and vector DBs (Qdrant, FAISS, Weaviate, pgvector)

• Construct RAG-based assistants, multi-turn memory, agent-based logic

📊 AI Product Features

• NLP tasks (NER, summarization, embeddings, retrieval, QA)

• Image/video model integration (optionally GANs, captioning, OCR)

• Build smart learning systems: time series forecasting, recommendations, knowledge graphs

• Integrate token tracking, user usage monitoring, analytics

🧑💻 Required Skills✅ Core

• 3–6+ years hands-on experience in AI/ML/LLM development and deployment

• Python (advanced), OOPs, NumPy, Pandas, SQL (MySQL/PostgreSQL)

• Data preprocessing, EDA, model training/tuning/evaluation

• ML algorithms (regression, classification, clustering)

• DL (ANN, CNN, RNN, LSTM, Transformer), GANs

• NLP (NER, summarization, sentiment, tokenization, embeddings)

✅ Dev & Ops

• FastAPI / Flask / Node.js (for backend API)

• Git, GitHub/Bitbucket, CI/CD pipelines

• Docker, Kubernetes, AWS (EC2, Lambda, S3, Sagemaker, etc.)

• Redis, Supabase, Firebase, PostgreSQL

• Git-based testing, debugging, and logging practices

✅ GenAI & LLM Stack

• LangChain, LlamaIndex, OpenAI APIs, Bedrock models

• Vector DBs (Qdrant, FAISS, pgvector)

• RAG architecture, memory layers, streaming AI

• Audio/Video AI tool integration (e.g., Whisper, AssemblyAI, Deepgram, WebRTC)

• Model fine-tuning, inference optimization (PEFT, LoRA, quantization)

🌟 Bonus (Nice to Have)

• Built, finetuned, trained or deployed LLM agents with real-world users or scale

• Experience in K-12 EdTech, AI tutors, or learning platforms

• Exposure to Lex, Connect, Step Functions, CloudWatch, IAM roles

• Familiarity with agent frameworks like LangGraph, AutoGen, CrewAI

• Basic frontend understanding (React, Next.js) to collaborate with full-stack teams

• Experience with Agentic memory layer, prompt engineering , Agentic tools, RAG systems, MCP servers, Google's A2A protocol , vertex AI .

🎓 Qualifications

• B.E./B.

Tech/M.

Tech in Computer Science, AI, Data Science, or related fields

• Strong coding and algorithmic reasoning skills

• Strong written communication and technical documentation ability

💼 Work Culture & Reporting

• Team: Report to CTO and Founder, collaborate with AI agents, backend, and UI/UX teams

• Culture: High ownership, agile delivery, startup intensity + deep tech creativity

• Tools we use: GitHub, Linear, Slack, GCP, Supabase, Notion, Vercel, LangChain, AWS

📩 How to Apply

Send your resume, GitHub, portfolio (if any), and links to AI/LLM projects you’ve built (not just notebooks) to:

📧 contact@yolearn.ai

🌐 www.yolearn.ai