Senior AI Engineer – GenAI Systems & AI Agent Architecture

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

Location

Type

Compensation

🚀 About YoLearn.ai

YoLearn.ai

Senior AI Engineer

🎯 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

AI/LLM projects you’ve built

📧 contact@yolearn.ai

🌐 www.yolearn.ai