Forward Deployed Engineers

• Solid background in software engineering or AI engineering, ideally with roots in solutions engineering, implementation, or embedded delivery work before AI made it fashionable\n
• 8+ years in software engineering overall\n
• Someone who tracks the AI space closely and adapts fast when the tools change\n
• 5+ years in full-stack development (Python and/or TypeScript, React, SQL)\n
• 2+ years hands-on shipping LLM or generative AI applications in production\n
• 1+ year working with agent orchestration frameworks (LangChain, LangGraph, or CrewAI)\n
• 1+ year designing RAG pipelines and working with vector databases\n
• Comfortable translating business needs into technical decisions with minimal hand-holding\n
• Genuine interest in where this field is heading, not just the current toolset\n
• Strong communication skills. You'll be the engineer customers see and trust\n

n Tech skills \n You keep pace with how fast AI is moving, and you have actually used the latest tools, not just read about them. \n

\n Core languages and backend \n \n

• Python: non-negotiable for AI orchestration, data scripting, and backend logic\n
• TypeScript / Node.js: essential for building customer-facing extensions, full-stack glue code, and lightweight web UIs\n
• SQL and Java/Go: needed for deep database queries and heavy enterprise system extensions\n
• FastAPI / Flask: for spinning up microservices and rapid integration layers\n

\n AI and agent orchestration \n \n

• LLM providers: fluency with Anthropic Claude, OpenAI, and open-source models via Hugging Face or vLLM\n
• Orchestration: production proficiency in LangChain or LangGraph and CrewAI for multi-step agent behaviours\n
• RAG and vector stores: hands-on design with vector databases (Pinecone, Qdrant, pgvector) and retrieval systems\n
• Evaluation and observability: deployment tracking using tools like LangSmith or Braintrust to audit hallucination rates and latency\n

\n Data and infrastructure \n \n

• Databases and warehouses: PostgreSQL, Snowflake, or BigQuery for customer data ingestion\n
• Cloud platforms: working knowledge of AWS, Google Cloud Platform, or Azure\n