Job Description
Job DescriptionWe’re looking for a Generative AI Engineer to be a strategic partner to product, a hands-on technical lead, and a core driver of what we build and how we build it. If you’re excited by building GenAI systems from 0 to 1—we’d love to talk.What You’ll Do
- Partner closely with product leadership to turn early ideas into prototypes, and prototypes into production-ready GenAI applications.
- Design and build full-stack AI systems, leveraging the latest LLMs (GPT-4, Claude, Mistral, LLaMA, etc.) and agentic frameworks (e.g., LangGraph, CrewAI).
- Own the 0-to-1 architecture for RAG, agentic workflows, and document intelligence pipelines—across retrieval, orchestration, prompting, and output handling.
- Experiment, build, and iterate rapidly, deploying MVPs and evolving them based on real user feedback.
- Build intelligent document processing (IDP) systems—extracting, understanding, and generating insights from complex documents.
- Evaluate and benchmark models - Rapidly test different models, prompts, and configurations to optimize performance for target tasks.
- Help shape our AI strategy and tech roadmap, staying on top of what’s emerging in AI and identifying what we should adopt early.
What We’re Looking For
- Collaborative and comfortable working in a fast-paced startup environment.
- Hands-on experience building GenAI products with real user-facing impact.
- Deep understanding of LLMs, prompt engineering, RAG, and agentic workflows.
- Familiarity with tools like LangChain, LlamaIndex, Hugging Face, and vector DBs (Pinecone, Weaviate, FAISS, etc.).
- Experience working with LLMs via APIs (OpenAI, Anthropic, etc.) and/or deploying open-source models.
- Strong software engineering best practices—you write clean, maintainable, production-grade code.
- Comfort with AWS cloud infrastructure
Bonus Points For
- Experience with intelligent document processing (IDP), form understanding, or document-based retrieval.
- Prior work using agentic frameworks (LangGraph, CrewAI, AutoGen).
- Knowledge of NLP techniques, embeddings, and LLM evaluation metrics.
- Background in applied ML/AI research or AI product development at an early-stage company.
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