Full Stack AI Engineer WFH

Important Notes Before Applying

• Work from home.
• Full-time role: this position requires complete commitment. Candidates planning to work two or more jobs simultaneously, or seeking part-time work, should not apply — such applications will be automatically rejected.
• Working hours: late nights in India, until approximately 4 AM IST, to overlap with US working hours. Do not apply if this schedule does not work for you.
• Compensation: depends on experience and current verifiable compensation (paychecks).
• Experience: candidates with 2–3 years of relevant experience are suitable.

About the RoleWe are looking for a highly capable Full Stack AI Engineer to architect, build, and scale modern SaaS platforms and production-ready AI-powered applications from the ground up.

This role is for someone who has real-world experience building complete SaaS products and implementing AI capabilities into production applications — not just experimenting with models or building isolated frontend and backend features.

The ideal candidate understands how to design, develop, and ship scalable, production-grade applications across the full stack, including:

• Frontend systems and user experiences
• Backend APIs and microservices
• Databases and data architecture
• Authentication, authorization, and user management
• Billing, subscriptions, and usage-based systems
• Infrastructure, deployment pipelines, and cloud environments
• Multi-tenant SaaS architecture
• Applied AI systems, model integration, and AI-powered workflows
• AI model deployment, inference, and performance optimization

You will work closely with product, design, AI, and infrastructure teams to build reliable, scalable software products and practical AI solutions used in production environments.

Responsibilities1. Full Stack Product Development

• Design and develop end-to-end SaaS applications from scratch
• Build responsive, scalable, and user-friendly frontend applications
• Develop secure backend APIs and microservices
• Implement authentication, authorization, RBAC, and user management systems
• Build billing, subscription, and usage-based systems
• Develop dashboards, admin panels, and workflow management systems
• Design scalable, multi-tenant application architectures

2. Applied AI & AI Engineering

• Design, develop, and integrate practical AI-powered features into SaaS products and business applications
• Work with open-source AI models and deploy them in real-world production environments
• Integrate Large Language Models (LLMs), embedding models, and other AI models into applications
• Build AI-powered workflows, intelligent automation systems, and agent-based applications
• Implement Retrieval-Augmented Generation (RAG) pipelines, vector search, and knowledge-based AI applications
• Develop AI application pipelines involving document processing, data extraction, classification, summarization, and information retrieval
• Integrate AI models through APIs and self-hosted inference environments
• Work with model serving, inference optimization, prompt engineering, and context management
• Implement AI evaluation, output validation, monitoring, and reliability mechanisms
• Optimize AI applications for latency, cost, scalability, and performance
• Collaborate with AI and infrastructure teams to deploy and maintain production-grade AI systems

3. Backend & API Engineering

• Design scalable backend architectures and APIs
• Build high-performance REST and/or GraphQL services
• Must be an expert in Python
• Demonstrate strong Node.js backend development expertise
• Implement asynchronous systems, queues, caching, and background jobs
• Optimize application performance, reliability, and scalability
• Integrate third-party APIs and services
• Build secure, maintainable, and well-documented backend systems

4. Database Engineering

• Design and manage scalable database systems
• Work extensively with:
• PostgreSQL
• MongoDB
• SQL-based systems
• Design database schemas, indexing strategies, query optimization, and data models
• Build systems supporting high availability, backups, migrations, and scaling
• Work with vector databases and vector search technologies for AI-powered applications

5. Infrastructure & Deployment

• Deploy and manage applications across cloud environments
• Work with:
• Docker
• CI/CD pipelines
• Cloud infrastructure
• Containerized deployments
• AI model serving and inference infrastructure
• Build scalable production environments with monitoring and observability
• Collaborate with DevOps and infrastructure teams on deployment strategies
• Understand the infrastructure requirements for deploying and scaling AI-powered applications

6. Engineering & Product Ownership

• Write clean, maintainable, and production-grade code
• Participate in architecture and technical design discussions
• Write technical documentation and implementation plans
• Review code and maintain engineering best practices
• Collaborate cross-functionally to ship features quickly and reliably
• Take ownership of features from initial development through production deployment and maintenanceRequirements

Must-Have

• Strong experience building complete SaaS platforms from scratch
• Strong expertise in Python and Node.js
• Practical experience working with open-source AI models
• Experience building and deploying AI-powered applications in real-world environments
• Deep full-stack engineering experience across frontend and backend systems
• Strong experience with databases, including PostgreSQL, MongoDB, MySQL, and SQL systems
• Experience designing scalable multi-tenant SaaS architectures
• Strong understanding of APIs, authentication systems, and application security
• Experience deploying and maintaining production systems in cloud environments
• Strong JavaScript/TypeScript and backend engineering skills
• Understanding of LLM integration, AI application development, and model inference
• Ability to take an AI capability or model and turn it into a usable, scalable product feature

Preferred

• Experience with modern frontend frameworks such as React or Next.js
• Hands-on experience with LLMs, open-source foundation models, and embedding models
• Familiarity with RAG architectures, vector databases, and semantic search
• Experience building AI agents, intelligent automation, and multi-step AI workflows
• Experience with model serving frameworks and inference optimization
• Familiarity with Redis, queues, caching systems, and event-driven architectures
• Familiarity with Kubernetes and distributed systems
• Experience with GPU-based AI workloads or cloud GPU infrastructure
• Startup or high-growth product experience

What We're Looking For

• Someone who has built real SaaS products used by customers
• Someone who has implemented and deployed practical AI solutions, beyond basic API integrations or experimental prototypes
• A strong product and systems-thinking mindset
• Ability to move quickly while maintaining engineering quality
• An ownership mentality with strong execution skills
• Someone comfortable working across the full stack and AI application layer without heavy supervision
• A problem-solver who can translate product requirements into reliable software and applied AI solutions
• Someone who understands that building production-ready AI products requires more than connecting an LLM API — it requires thoughtful engineering, integration, evaluation, optimization, and scalability