AI Engineer – Rapid Prototyping, Automation & Product Engineering

Job Summary

We are seeking a skilled AI Engineer with 2–4 years of experience in generative AI, LLM orchestration, and rapid prototyping to join our dynamic product engineering team. You will design and develop AI-native products and automation workflows, leveraging low-code/no-code platforms and backend integrations to accelerate product delivery. You will build scalable, modular GenAI systems, including multi-agent architectures and RAG pipelines, enabling seamless AI-powered workflows from prototype to production. Collaboration with cross-functional teams (designers, product managers, engineers) is key to translating ambiguous concepts into MVPs that delight users. The ideal candidate has hands-on experience with Python, Node.js, FastAPI, AI frameworks (LangChain, Hugging Face), and automation tools (n8n, Zapier), coupled with a strong product mindset and excellent communication skills. Experience in AI observability and prompt engineering is a plus.

Job Roles & Responsibilities

• Rapid Prototyping: Build and iterate on clickable UI prototypes and AI workflows using no-code/low-code platforms to validate product ideas quickly.
• AI Workflow Automation: Design, deploy, and maintain end-to-end automation pipelines with tools like n8n, Zapier, or custom solutions, ensuring error handling and monitoring.
• Backend Development & Integration: Develop lightweight backend services (serverless functions, FastAPI, Express) and integrate third-party APIs (CRM, CMS, analytics, authentication) to support AI-driven products and prototypes.
• Generative AI & LLM Engineering: Architect and implement generative AI solutions including RAG pipelines, multi-agent systems, and prompt engineering to build scalable, production-grade AI features.
• Cross-Functional Collaboration: Work closely with product managers, designers, and senior engineers to translate ambiguous product concepts into MVP feature sets and scalable solutions.
• Product Strategy & Execution: Independently evaluate product opportunities, define MVP scope, and iterate rapidly based on user feedback and metrics.
• AI Observability & Monitoring: Build and maintain frameworks for AI model evaluation, tracing, and red-teaming to ensure robustness and reliability in production environments.
• Documentation & Demos: Prepare clear technical documentation, demo prototypes to stakeholders, gather feedback, and continuously improve solutions.
• Team Leadership & Mentoring: Guide junior engineers, establish best practices in AI development, and drive innovation within the team.

Cultural Expectations

• Deliver Rapid Prototypes: Quickly build and iterate AI-powered prototypes and workflows using no-code/low-code tools and custom backend services, demonstrating business value early.
• Drive Automation Excellence: Develop robust, maintainable automation pipelines that integrate diverse systems, ensuring smooth and reliable AI operations in production.
• Technical Ownership: Take end-to-end responsibility for AI components from concept to production, including backend services, API integrations, and AI orchestration.
• Innovate with Generative AI: Leverage cutting-edge GenAI models, multi-agent frameworks, and prompt engineering techniques to create scalable and efficient AI products.
• Collaborate Effectively: Partner closely with designers, PMs, and engineers to align technical solutions with business goals and deliver MVPs on tight timelines.
• Communicate Clearly: Present prototypes and technical ideas effectively to both technical and non-technical stakeholders; document work thoroughly.
• Adapt & Learn Quickly: Stay updated on the latest AI technologies and best practices; be ready to adopt new tools and frameworks that improve product delivery.
• Maintain Quality: Implement testing and monitoring strategies to ensure AI models and automation workflows are reliable, secure, and performant.
• Mentor & Lead: Support junior team members through knowledge sharing, code reviews, and best practices to elevate team capabilities.

Hiring Process

• Technical assignment tailored for the role.
• Interview with hiring manager to assess technical skills.
• Live coding task to evaluate coding proficiency.
• Technical interview with senior member from the technical team.
• Final interview with Tech Lead/CTO.