lever
Visiting Engineer
AIFund
USA
Contract
Mid
Onsite
USD 10000-12000 monthly / month
Discovered 1 weeks ago
CursorWindsurfGitHub CopilotClaude CodePineconeWeaviate
Free
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Key Skills for This Role
CursorWindsurfGitHub CopilotClaude CodePineconeWeaviate
Full Job Posting
- Drive rapid engineering efforts to build full-stack applications using GenAI tools, enabling deep exploration of product ideas, user experiences, and technical feasibility
- Apply GenAI building blocks (prompt engineering, graphDBs, vectorDBs, agentic frameworks, evals, and guardrails) to real-world development
- Make key decisions around infrastructure and platform choices, balancing short-term prototyping needs with long-term scalability
- Collaborate cross-functionally with product, design, and AI experts to create, test, and iterate on new concepts
- Use AI-assisted coding tools (e.g., Cursor, Windsurf, GitHub Copilot, Claude Code) to enhance productivity and speed of iteration
- Gather user feedback and iterate quickly based on insights to improve usability and effectiveness
- Deploy and manage applications on cloud infrastructure (e.g., AWS, GCP, Supabase)
- Build and integrate APIs and third-party services
- Participate in architecture discussions and technical planning
- Identify and troubleshoot issues across the stack
- Contribute to improving development processes, tools, and team practices
- Stay current with industry trends and emerging technologies
- Demonstrated experience building applications that incorporate Generative AI
- Proficiency with GenAI tools and frameworks: prompt engineering, Retrieval-Augmented Generation (RAG), vector databases (e.g., Pinecone, Weaviate), agentic platforms (e.g., LangGraph, CrewAI), LLM evals, and guardrails
- Experience with front-end technologies like JavaScript, HTML, CSS, and modern frameworks
- Strong back-end skills, particularly with Python
- Familiarity with SQL and NoSQL databases (PostgreSQL, MongoDB, etc.)
- Hands-on experience using AI-assisted coding tools (e.g., Cursor, Claude Code) and understanding of best practices for effective use
- Proven track record of architecting, implementing, and deploying scalable and maintainable AI-powered systems
- Expertise in designing end-to-end system architectures involving front-end, back-end, APIs, and AI/ML components
- Deep curiosity and a fast-learning mindset—especially around emerging trends in AI
- Ability to write scrappy, disposable code for fast prototyping, and clean code for scalable products
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