Applied AI Engineer

1-3 years experience in applied ML or LLM research or engineering. Demonstrated experience building agentic systems using tools like LangGraph, CrewAI, n8n and/or flowise, or LangChain. Deep familiarity with RAG, Graph-RAG, vector stores, and dynamic tool use orchestration. Strong Python proficiency and experience with GCP, SQL, and DBT. Foundation in statistics, including hypothesis testing, regression, and time series analysis. Demonstrated experience applying NLP and transformer-based models in production workflows.

Design and build AI pipelines using frameworks like LangGraph, CrewAI, n8n, and LangChain. Build and scale RAG, Graph-RAG, and custom fine-tuned LLM solutions. Develop agent patterns that can reason over tools, retrieve context, and persist goals. Collaborate with engineers, product managers, and domain experts. Contribute to internal frameworks and standards for evaluating and debugging agents. Drive continuous experimentation with memory systems, vector search, and knowledge graph integration. Participate in agent simulation testing and contribute to MCP-based design strategies.