Generative AI Engineer (GenAI Engineer)

Role: Generative AI Engineer (GenAI Engineer)

Location: USA PST time zone (Remote / Hybrid ). Time zone to follow: PST

Job description :

• 2–3 years of software development experience, including candidates with strong academic, internship, personal, or open-source project experience.
• Strong programming fundamentals in Python, JavaScript/TypeScript, Node.js, or Golang.
• Basic hands-on experience with React, Next.js, REST APIs, Git, and GitHub.
• Hands-on familiarity with AI-assisted development tools such as Cursor, Claude Code, GitHub Copilot, ChatGPT, Windsurf, or Replit AI.
• Fundamental understanding of Generative AI, LLMs, Prompt Engineering, AI Agents, and RAG.
• Basic understanding of MCP (Model Context Protocol) and how AI agents can interact with tools, APIs, and external systems.
• Exposure to building AI/LLM-based applications, chatbots, workflows, or proof-of-concepts through academic, internship, personal, or open-source projects.
• Understanding of APIs, JSON, web services, and basic application architecture.
• Familiarity with Git-based development and software engineering practices.
• Strong analytical and problem-solving skills with the ability to learn new technologies quickly.
• Strong curiosity and enthusiasm for experimenting with emerging AI tools, frameworks, and developer productivity technologies.

Preferred / Good-to-Have Skills:

• Exposure to LangChain, LangGraph, CrewAI, AutoGen, or similar AI/agent frameworks.
• Familiarity with vector databases, embeddings, semantic search, and RAG implementations.
• Basic exposure to Docker, Kubernetes, AWS, Azure, or GCP.
• Experience integrating AI applications with enterprise systems, databases, or third-party APIs.
• Experience with MCP servers, tool calling, function calling, or agentic workflows.
• Personal projects, GitHub contributions, hackathons, or academic projects involving GenAI, LLMs, or AI agents.
• Ability to use AI coding tools to rapidly convert ideas into functional prototypes or demos.
• Strong product mindset and ability to understand a problem and translate it into a working technical solution.
• Comfortable working in a fast-paced environment involving rapid experimentation, ambiguity, and continuous learning.
• Ability to balance rapid development with code quality, security, maintainability, and engineering best practices.