Full-Stack AI Engineer

Overview

AI Marketplace is an inclusive ecosystem with a mission to educate and build AI for all. With a thriving community of over 300,000 members, we offer a community-driven learning platform and an AI marketplace. Our AI marketplace is making AI accessible to all by providing secured and reliable APIs to a vast collection of pre-built AI models developed by the best data scientists worldwide. This unique ecosystem is designed to empower companies and individuals to seamlessly learn, develop, and implement AI solutions to challenges of humanity.

As we continue to make an impact, we are looking forward to welcoming our new colleagues, who can join the forces to make an impact to the next billion people or the next 10k+ organizations.

Key Responsibilities:

• Design, develop, and maintain scalable end-to-end agentic/GenAI/AI full-stack applications.
• Develop robust backend services, APIs, and microservices.
• Write clean, maintainable, and well-tested code following best practices.
• Participate in system design discussions and architecture decisions.
• Debug, troubleshoot, and optimize application performance.
• Contribute across the complete software engineering and AI lifecycle, including: Requirement analysis, System design, Development, Testing, Deployment and Monitoring & maintenance
• Build reliable GenAI or LLM-powered applications end to end.
• Manage data ingestion and maintain database integrity
• Building agents from scratch and understanding of Multi-Agent architecture is a must.
• Keep abreast of the latest advancements in LLMs and Agentic AI.

Technical Acumen:

• Must have strong programming skills, particularly in Python.
• Understanding of REST APIs and microservices architecture.
• Strong foundations of backend knowledge (REST APIs, databases).
• Knowledge of LLM frameworks such as Langchain, LlamaIndex, OpenAGI, CrewAI, and AutoGen.
• Knowledge of Data Engineering Practices: Skills in data preprocessing, cleaning, transformation, etc.
• Should be familiar with Agents, RAG (retrieval augmented generation), COT prompting techniques, etc
• Strong analytical skills to troubleshoot and solve complex problems that arise in AI application development.
• Understanding how AI features integrate into user-facing products.