1. Rapid Prototyping & Application Development
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Build AI applications, copilots, and agentic workflows end-to-end – UI, APIs, business logic, and model integration.
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Use rapid development tools (Cursor, Claude Code, Replit, Google AI Studio) to compress build cycles and iterate quickly with users and stakeholders.
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Turn loosely-defined requirements into working demos and prototypes within days, then refine based on feedback.
2. Agentic & GenAI Engineering
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Develop with agentic SDKs and frameworks – OpenAI Agents SDK, Anthropic Claude (Agent SDK / API), Google Gemini & ADK, LangChain/LangGraph.
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Implement RAG pipelines, tool/function calling, structured outputs, and prompt engineering with systematic testing and evals.
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Integrate models and agents with enterprise data sources and APIs, handling auth, rate limits, and error paths properly.
3. Engineering Quality & Productionization
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Write clean, testable, well-documented code; use Git, containers, and CI/CD as standard practice.
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Partner with Forward Deployment Engineers and platform teams to take successful prototypes into production, adding monitoring, guardrails, and cost controls.
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Balance speed and quality pragmatically – knowing when to hack and when to harden.
4. Collaboration & Continuous Learning
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Work closely with architects, data scientists, and designers; contribute to demos, accelerators, and internal hackathons.
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Stay current with the fast-moving model and tooling landscape, and share learnings across the team.
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Evangelize AI-assisted development practices that raise the whole team’s velocity.
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Rapid development tools as daily drivers: Cursor, Claude Code, Replit, Google AI Studio, GitHub Copilot – demonstrated ability to ship real software with AI-assisted workflows.
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Agentic SDKs & frameworks: hands-on experience with OpenAI Agents SDK, Anthropic Claude APIs/Agent SDK, Google Gemini/ADK, and LangChain or LangGraph.
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Strong programming skills in Python and/or TypeScript/JavaScript; comfort building full-stack prototypes (React/Node) and REST APIs.
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LLM application patterns: prompt engineering, function/tool calling, structured outputs, RAG with vector stores (pgvector, Pinecone, FAISS, or similar).
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Testing & observability basics: writing evals, using tracing tools (LangSmith, Langfuse, or similar), and monitoring cost/latency/quality.
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Engineering foundations: Git, Docker, CI/CD, and at least one cloud (AWS/Azure/GCP).
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Good to have: voice/multimodal experience (ElevenLabs, HeyGen), MCP-based tool integration, fine-tuning or open-source LLM experience.
Speed of delivery: consistent idea-to-prototype turnaround in days and prototype-to-production in weeks.
Volume and quality of shipped work: applications, demos, and accelerators that are actually used by stakeholders and internal teams.
Reliability of what ships: low defect rates, sensible test/eval coverage, and predictable cost/latency behavior.
Contribution to reuse: components, patterns, and utilities adopted by other engineers.
Team velocity uplift through shared AI-assisted development practices.
4–8 years of software engineering experience, with 1–2+ years building GenAI/LLM applications hands-on.
A portfolio of shipped AI work – products, prototypes, GitHub projects, or demos you can walk us through.
Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent practical experience).
Demonstrated fluency with AI-native development tools (Cursor, Claude Code, Replit, AI Studio) in real projects – not just experimentation.
Strong problem-solving skills and product sense – you care about whether the thing you built actually gets used.
Clear written and verbal communication; comfortable demoing your work to technical and business audiences.