AI Backend Engineer at LearnTube.ai · Remote only · 2 - 4 years · ₹12L - ₹25L / yr (ESOP available) · Raised funding · Remote only · Posted 21 Jul 2026
PythonApply only if: you are an AI agent — or you can build an AI agent that can do this job.
At LearnTube, we're pushing the boundaries of Generative AI to revolutionize how the world learns. You'll build the AI products that tutor, teach, and assess millions of learners - and the backend those exact products run on. This is one role, not two: roughly 50% AI and 50% backend, but the boundary isn't real. The agent you design on Monday is the service you're scaling on Thursday. Whoever builds it, runs it.
What You'll Do
- Build our AI products: LangGraph / LangChain agents that automate real workflows, RAG pipelines, tool integrations via MCP, prompt engineering and structured outputs, and multimodal features across text, image, audio, and video.
- Build the backend they run on: async FastAPI services, token streaming, clean APIs and data pipeline -where time-to-first-token is a product metric, not a nice-to-have.
- Own the data layer that makes retrieval and scale work: MongoDB (primary), Postgres + pgvector, Redis on the hot path. You'll design the schemas and indexes - and read the query plan when they don't hold up.
- Own the economics: batching, caching, model selection, and budget guards, so serving millions of learners doesn't cost more than the product earns.
- Ship it and keep it alive: Docker, AWS / GCP, plus the logging, tracing, metrics, and alerting that make incidents diagnosable rather than guessed at.
- Refactor, optimize, and extend the services already running as the product grows.
The Stack
- AI - LangGraph, LangChain, MCP, RAG, embeddings & vector search, structured outputs, evals, frontier LLM APIs (text + multimodal)
- Backend - Python (async/await, typing), FastAPI, SSE/streaming, MongoDB (primary), Postgres + pgvector, Redis
- Infra - Docker, AWS / GCP, CI/CD, monitoring & alerting
You won't have deep experience in all of it - nobody does. We care more about how fast you pick up what you haven't touched.
What We're Looking For
- Someone who lives in the overlap: you've built with LLMs or agents, and you've kept something you built running at scale. That combination is rare, and it's exactly the job.
- Strong Python and FastAPI, with Docker and production deployment.
- Real fluency with at least one database's failure modes, not just its syntax: you've debugged a slow query, read an execution plan, and fixed the index rather than the symptom.
- Debugging instinct and judgment about depth. You read the logs before you edit the code — and you know the difference between a fix that turns the alert green and one that makes the problem stop existing.
- A builder's bias: you ship, measure, and iterate.
How We Hire
No take-home. No whiteboard trivia. You'll get a timed, hands-on incident simulation: real production failures in a real repo, with the logs, the dashboard, and the code. There's no single right answer - every incident can be fixed at several depths, and we're reading how you debug and how deep you go. Here's the link for you to get started on it - https://workat.learntube.ai/ai-engineer?utm_source=cutshort
The Team
Google's Top 20 Startups to Watch. Google AI First Accelerator '24. Backed by funds of Naval Ravikant, Reid Hoffman, and founders/CXOs from Udemy, Flipkart, Jupiter, PayU, Edmodo & Inflection AI. Featured on CNBC-TV18. 11–50 people building something that changes how people learn, permanently.
Why work with us: state-of-the-art generative AI on a backend you own. Full ownership from ideation to deployment. Direct access to founders and advisors, including the CTO of Inflection AI. Three years of growth packed into one, and Monday morning meetings you actually look forward to.
Shubham Vishwakarma
Full Stack Developer - Averlon
About LearnTube.ai
About
At LearnTube, we're reimagining how the world learns making education accessible, affordable, and outcome-driven using Generative AI. Our platform turns scattered internet content into structured, personalised learning journeys using:
- AI-powered tutors that teach live, solve doubts instantly, and give real-time feedback
- Frictionless delivery via WhatsApp, mobile, and web
- Trusted by 3.2 million learners across 64 countries
Founded by Shronit Ladhani and Gargi Ruparelia, both second-time entrepreneurs and ed-tech builders:
- Shronit is a TEDx speaker and an outspoken advocate for disrupting traditional learning systems.
- Gargi is one of the Top Women in AI in India, recognised by the government, and leads our AI and scalability roadmap.
Together, they bring deep product thinking, bold storytelling, and executional clarity to LearnTube’s vision. LearnTube is proudly backed by Google as part of their 2024 AI First Accelerator, giving us access to cutting-edge tech, mentorship, and cloud credits.
Tech stack
Candid answers by the company
At LearnTube, we’re on a mission to make learning accessible, affordable, and engaging for millions of learners globally. Using Generative AI, we transform scattered internet content into dynamic, goal-driven courses with:
- AI-powered tutors that teach live, solve doubts in real time, and provide instant feedback.
- Seamless delivery through WhatsApp, mobile apps, and the web, with over 1.4 million learners across 64 countries.
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Job Description – AI Engineer (End-to-End Development & Deployment)
Role Summary
We are looking for an AI Engineer with hands-on experience in designing, developing, deploying, and maintaining Generative/Agentic AI solutions in production. The ideal candidate should have end-to-end ownership of AI applications, from development to deployment, monitoring, and optimization.
Key Responsibilities
● Design, build, and deploy Generative/Agentic AI solutions.
● Develop applications using LLMs, RAG, AI agents, and vector databases.
● Build scalable APIs and integrate AI solutions with enterprise applications.
● Implement CI/CD pipelines, containerization, and MLOps best practices.
● Monitor, optimize, and maintain production AI systems.
● Collaborate with cross-functional teams to deliver business-driven AI solutions.
Required Skills
● Strong programming skills in Python.
● Experience with vector databases (e.g., Pinecone, FAISS, ChromaDB) and graph memory systems
● Knowledge of atleast one agent development framework: Google ADK (preferred), LangChain/LangGraph/LlamaIndex, CrewAI
● Experience with LLMs, RAG, GenAI, AgenticAI Agents
● Hands-on experience with FastAPI, and REST APIs.
● Knowledge of Docker, Kubernetes, Git, CI/CD.
● Experience with AWS, Azure, or GCP.
● Experience with security compliance, monitoring and observability tools such as AWS CloudWatch, Azure Monitor, Google Cloud Monitoring.
PythonJob Description – Python & Generative AI Engineer
Python & Generative AI Engineer
Location: Bangalore, India
Experience: 4+ Years
Employment Type: Full-time
Job Summary
We are looking for an experienced Python & Generative AI Engineer with 4+ years of software development experience and strong hands-on expertise in Python, LLMs, Generative AI, and AI application development.
The ideal candidate should be comfortable building production-grade AI solutions, integrating LLMs with enterprise applications, and developing scalable APIs and services using Python.
Key Responsibilities
- Design, develop, and deploy Generative AI applications using Python and modern AI/ML frameworks.
- Work with Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, or similar models.
- Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases and embedding models.
- Build AI-powered applications using frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
- Develop scalable REST APIs and backend services using FastAPI, Flask, or Django.
- Integrate LLM APIs, prompt engineering, function/tool calling, and structured outputs into enterprise applications.
- Work with vector databases such as FAISS, Pinecone, Weaviate, Milvus, or Azure AI Search.
- Implement document processing, chunking, embeddings, semantic search, and knowledge retrieval solutions.
- Evaluate LLM responses for accuracy, relevance, hallucination, latency, and cost.
- Build and maintain production-ready AI pipelines with appropriate monitoring, logging, security, and error handling.
- Collaborate with data scientists, ML engineers, software engineers, and business stakeholders to deliver AI solutions.
- Write clean, reusable, testable, and well-documented Python code.
- Participate in architecture discussions, code reviews, testing, deployment, and production support.
Required Skills
- 4+ years of experience in software/Python development.
- Strong proficiency in Python and object-oriented programming.
- Hands-on experience with Generative AI and LLM-based applications.
- Experience with OpenAI/Azure OpenAI, Anthropic, Google Gemini, or open-source LLMs.
- Strong understanding of Prompt Engineering and LLM application patterns.
- Experience implementing RAG pipelines.
- Knowledge of embeddings, vector databases, semantic search, and document retrieval.
- Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks.
- Strong experience developing REST APIs using FastAPI/Flask/Django.
- Familiarity with Git, CI/CD, Docker, and cloud platforms such as AWS, Azure, or GCP.
- Good understanding of SQL and databases.
- Strong problem-solving and communication skills.
Good to Have
- Experience with AI agents / Agentic AI and tool/function calling.
- Experience with multi-agent frameworks.
- Knowledge of MLOps/LLMOps and model evaluation.
- Experience with Kubernetes and containerized deployments.
- Knowledge of NLP, machine learning, or deep learning.
- Experience with Azure AI, AWS Bedrock, Amazon SageMaker, or Google Vertex AI.
- Understanding of responsible AI, data privacy, and LLM security.
Education
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
PythonAI Engineer
LLMs, Agents & AI Services
📍 Mumbai (On-site) | Full-time | 2-4 years
About the Role:
Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.
AI is core to how we design, deliver, and scale software for our customers.
We are hiring an AI Engineer for a dedicated client engagement building a complex production AI platform, working on the AI capabilities and agentic features at the core of the product.
The mandatory requirement for this role is at least one AI feature personally shipped to production for real users, with operational ownership.
The role suits someone who thinks quickly on solutioning, can take an ambiguous problem to a working prototype in days, and has the discipline to carry it through to production with predictable economics.
You will work alongside the Senior AI Engineer and the wider pod, with ownership of parts of the AI surface area of the product.
Responsibilities:
Solutioning and POCs
Translate ambiguous customer problems into working POCs at speed.
Pick the right model, framework, and architecture, and demonstrate value early before scaling investment.
LLM Application Development
Build AI features and services using LLM APIs from OpenAI, Anthropic, Google, and self-hosted open-weight models (Llama, Qwen, Mistral).
Choose the right model per use case based on cost, latency, capability, and context-window trade-offs.
Agentic System Design
Design and implement agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, or custom orchestration.
Cover tool use, planning, memory, and multi-step reasoning appropriate to the problem.
API and Service Development
Build production AI services and APIs using Python and FastAPI.
Handle streaming responses, async processing, structured outputs, retries, and graceful degradation when models or tools fail.
Retrieval and Tool Integration
Implement RAG pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma), embeddings, chunking strategies, hybrid search, and reranking.
Integrate external tools, internal APIs, and document sources through tool-calling and MCP-style patterns.
Cost Analysis and Unit Economics
Model the per-request and per-user cost of every AI feature before it ships.
Track token usage, prompt caching, batching, and model-routing strategies.
Drive measurable improvements in unit economics.
Production Hardening
Add observability and tracing (LangSmith, Langfuse, OpenTelemetry), guardrails, content safety checks, prompt injection defences, and fallback behaviour.
Prompt Engineering and Evaluation
Design, test, and iterate prompts with measured outcomes.
Build evaluation harnesses for accuracy, hallucination, latency, and cost.
Run benchmarks across models and prompt variants before locking in a design.
Requirements:
AI Feature Shipped to Production (Mandatory)
Must have personally built and shipped at least one AI feature that runs in production for real users, with operational ownership.
POCs, internal demos, and one-off scripts do not qualify.
2 to 4 Years of Professional Software or AI Engineering Experience
With at least one production AI feature owned end to end.
Strong Python Proficiency and API Development with FastAPI
Comfort with type hints, async, packaging, testing, streaming responses, and authentication.
Production-grade Python, not notebook-only code.
Hands-on Depth Across the LLM and Agent Stack
Working experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or self-hosted open-weight models (vLLM, Ollama, Together, Replicate).
Working familiarity with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.
Working knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma).
Solutioning Speed and POC Velocity
Demonstrated ability to move from a fuzzy problem to a working prototype in days.
Strong instinct for what to build first, what to defer, and what to throw away.
Cost Discipline for Production AI
Ability to calculate, monitor, and optimise the cost of LLM APIs, tokens, embeddings, vector store usage, and infrastructure.
Treats unit economics as a first-class concern.
AWS Familiarity
Working knowledge of EC2, S3, IAM, and at least one of Bedrock, SageMaker, or equivalent.
Comfortable in a Fast-Moving Environment
Self-directed, comfortable with ambiguity, takes ownership without being asked, and ships under shifting priorities.
Strong Written and Spoken English Communication
Able to explain trade-offs to non-AI engineers, designers, product managers, and clients in plain language.
Nice to Have
- fine-tuning or LoRA, QLoRA, PEFT exposure
- MCP server authoring
- eval framework experience (LangSmith, Promptfoo, Ragas, DeepEval)
- open-source AI contributions
- multi-modal models (vision, audio)
PythonProduct Engineer — Role Summary
We are looking for a Product Engineer to build user-facing products that combine AI capabilities with practical applications. You will work at the intersection of software engineering and AI, developing autonomous, agent-driven systems that solve complex educational and research problems.
Key Responsibilities:
- Product Development: Design, build, and deploy production-ready applications powered by LLMs and AI agents.
- Data Engineering: Build scalable ETL/ELT pipelines to process structured and unstructured data, including text and audio, for RAG and model fine-tuning.
- Agentic Workflows: Develop multi-step AI agents with tool calling, APIs, databases, search, reasoning, and memory.
- Rapid Prototyping: Turn ideas and research concepts into interactive, production-ready applications.
- AI Integration: Use frameworks such as LangChain, LlamaIndex, AutoGen, or custom orchestrators to integrate AI into scalable systems.
- User Experience: Transform raw AI outputs into reliable, intuitive, and responsive user experiences.
- Collaboration: Work closely with ML researchers and data engineers to integrate custom and fine-tuned models.
- Observability: Monitor agent behavior, manage edge cases, reduce hallucinations, and improve reliability in production.
The ideal candidate combines strong software engineering, AI/LLM expertise, data engineering, and product thinking, with the ability to take an AI concept from prototype to production.
PythonKey Responsibilities
- Design, develop, and maintain scalable applications using Python.
- Build and integrate AI-powered solutions into existing applications.
- Develop REST APIs and backend services.
- Work with AI/ML models, LLMs, and Generative AI technologies where applicable.
- Integrate AI services and APIs into business applications.
- Write clean, maintainable, and efficient code.
- Collaborate with cross-functional teams to deliver high-quality solutions.
- Troubleshoot issues and optimize application performance.
Must-Have Skills
- Strong hands-on experience in Python development.
- Good understanding of Python frameworks such as FastAPI, Flask, or Django.
- Experience in REST API development and backend services.
- Knowledge or experience in AI/ML concepts.
- Understanding of application development, debugging, and problem-solving.
- Good understanding of databases and data handling.
Good-to-Have Skills
- Hands-on experience with Generative AI and Large Language Models (LLMs).
- Experience with AI APIs and frameworks such as LangChain or LlamaIndex.
- Knowledge of Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
- Familiarity with cloud platforms such as AWS, Azure, or GCP.
- Experience with Docker, Kubernetes, or CI/CD pipelines.
Role: AI Developer
Experience: 3–4 Years
Employment Type: Full-Time
Location: Goregaon, Mumbai
About the Role
We are looking for an experienced AI Developer with 3–4 years of software development experience and strong hands-on exposure to Generative AI, AI Agents, Copilots, and AI-powered application development.
The candidate will be responsible for building production-ready AI solutions, developing agentic workflows, modernizing legacy applications, and integrating LLM capabilities into enterprise applications.
Key Responsibilities
- Design, develop, and deploy AI Agents and agentic workflows for enterprise use cases.
- Build AI Copilots and LLM-powered applications using modern AI frameworks and APIs.
- Develop RAG-based applications using embeddings, vector databases, and enterprise data.
- Work on legacy application migration and modernization, leveraging AI-assisted development and code transformation techniques.
- Analyze legacy codebases and design strategies for AI-driven migration, refactoring, and modernization.
- Integrate LLMs with enterprise applications, APIs, databases, and third-party systems.
- Implement tool calling, function calling, multi-agent workflows, and workflow automation.
- Perform prompt engineering, context optimization, model evaluation, and AI application testing.
- Take ownership of AI solutions from POC and prototyping through production deployment.
- Collaborate with product managers, architects, and engineering teams to convert business requirements into scalable AI solutions.
- Stay updated with emerging technologies in Generative AI, Agentic AI, LLMs, and AI-assisted software development.
Required Skills
- 3–4 years of professional software development experience.
- Strong proficiency in Python and/or JavaScript/TypeScript.
- Hands-on experience developing Generative AI / LLM-based applications.
- Strong understanding of AI Agents, RAG, Prompt Engineering, LLM APIs, and embeddings.
- Experience with frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent.
- Experience working with REST APIs, databases, Git, and cloud environments.
- Hands-on experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or equivalent.
- Good understanding of software architecture, debugging, testing, and deployment practices.
Good to Have
- Experience with Microsoft Copilot / Copilot Studio.
- Experience working with Claude, OpenAI, Gemini, Azure OpenAI, or open-source LLMs.
- Experience in legacy application migration, modernization, or code conversion.
- Knowledge of Azure AI / AWS / Google Cloud AI services.
- Experience with MCP, multi-agent systems, tool calling, and AI orchestration.
- Experience building enterprise-grade AI solutions with focus on security, scalability, and performance.
PythonRole Overview
We are looking for a Python Developer with strong experience in Generative AI and LLM-based applications. The candidate should have hands-on experience building AI solutions using Python, RAG, LangChain/LangGraph, and related GenAI technologies.
Mandatory Skills
Python, GenAI/LLM, RAG, LangChain/LangGraph, Agentic AI, FastAPI, REST API, Vector Database, Prompt Engineering, Microservices
Key Responsibilities
- Develop and maintain applications using Python and modern frameworks.
- Build GenAI/LLM-based applications and solutions.
- Develop RAG pipelines using vector databases.
- Work with LangChain/LangGraph for LLM and agent-based applications.
- Develop and integrate REST APIs using FastAPI.
- Implement Agentic AI workflows and AI-powered features.
- Integrate LLMs with existing applications and microservices.
- Apply prompt engineering techniques to improve AI application performance.
Machine Learning (ML)
Deep LearningPython🚀 WE’RE HIRING | AI/ML GENERATIVE AI ENGINEER
📍 Location: Remote
💼 Experience: 5+ Years
🔄 Position: Contract – Extendable
🔹 ROLE HIGHLIGHTS
➤ Build and deploy AI/ML amp; Generative AI solutions
➤ Develop LLM, RAG, NLP, Recommendation amp; Predictive solutions
➤ Work on AI Agents, Chatbots, Computer Vision amp; Content Intelligence
➤ Build ML models using PyTorch, TensorFlow, Keras amp; Scikit-learn
➤ Develop RAG solutions using LangChain, LlamaIndex, FAISS/Milvus
➤ Integrate OpenAI, Azure OpenAI, AWS Bedrock, Vertex AI amp; Hugging Face
➤ Build scalable AI APIs using Python, FastAPI/Flask/Django
➤ Contribute to Private AI amp; Smart Agentic Systems
⚙️ MUST-HAVE SKILLS
◆ Python – 3+ years
◆ AI/ML – 5+ years
◆ Generative AI – 2+ years
◆ LLMs, RAG, Embeddings , Transformers
◆ ML/DL, NLP amp; Predictive Analytics
◆ Cloud AI Platforms – Azure / AWS / GCP
◆ AI/ML Deployment | MLOps
Interview Process - F2F Round at Pune Location
PythonJob Title: Full Stack AI Engineer
Location: Remote/Hyderabad
Experience Level: 3-5
Salary Range: 12-18LPA
Application Link:https://beyond.ciltriq.com/apply/BUILD
Description:
Join a team building AI-powered systems that solve complex business problems and automate operational workflows across document processing, voice agents, enterprise integrations, workflow automation, and multi-agent systems.
Strong full-stack foundations: frontend state management, asynchronous user experiences and performance; backend API design, authentication, data modelling, databases, queues and distributed systems.
Strong coding ability in Python and JavaScript or TypeScript, with practical experience in modern frontend frameworks and backend services.
Requirements:
- Design and build complete systems: frontend applications, backend services, APIs, databases, data pipelines and integrations with customer systems.
- Build multi-agent workflows with clear agent responsibilities, tool access, shared state, context management, routing, handoffs and coordination across sequential and parallel tasks.
- Make agent execution dependable through durable state, checkpoints, retries, timeouts, idempotency, recovery and human approval or review where needed.
- Deliver document-processing pipelines, voice agents and retrieval-based AI applications, connecting model outputs to useful actions in real business workflows.
- Own quality in production: automated tests, AI evaluations, guardrails, observability, access controls, deployments, incident response and clear documentation.
- Choose where AI adds value and where deterministic software is the better fit. Balance accuracy, latency, cost, security and maintainability.
- Improve reusable engineering foundations, review code and help other engineers grow as the team expands.
- A solid understanding of tool calling, structured outputs, retrieval, context and memory management, model selection and evaluation.
- Practical cloud and deployment experience, including containers, CI/CD, secrets management, logging, monitoring and production debugging.
- Ability to reason from first principles, investigate failures across system boundaries and communicate technical decisions clearly to customers and teammates.
- Useful additional experience: Document AI and OCR, real-time voice systems, enterprise integrations, agent protocols such as MCP, and orchestration frameworks.
- Useful additional experience: Mentoring engineers or building reusable platforms.
Role Overview
We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale.
Key Responsibilities
Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns. Build and productionise RAG pipelines, prompt-based applications, and LLM integrations across providers. Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring. Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers. Implement guardrails, prompt-injection defences, and evaluation frameworks to keep AI systems safe and reliable in production.
Write clean, tested, production-grade code and participate actively in code and design reviews.
Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions. Troubleshoot and optimise AI systems for accuracy, latency, and cost in production.
Required Qualifications
8–12 years of hands-on software engineering experience, with a strong, unbroken technical track record. Hands-on experience building and shipping AI/ML systems in production, not just POCs.
Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel).
Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers.
Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices. Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation. Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases. Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, and observability.
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon