Role overview
- Build specialized agents within multi-agent pipelines designed for handling complex research problems, developing durable outputs, and taking action in external systems on the user's behalf
- Build retrieval systems that find relevant prior conversations and extracted facts across a user's history
- Design embedding and indexing strategies for conversation-derived content
- Build and optimize relevance ranking for memory retrieval
- Build LLM-based memory capabilities: conversation summarization, cross-session context retrieval, and persistent user preference extraction
- Instrument pipelines for observability, tracing, and quality monitoring
- Collaborate with evaluation engineers on quality measurement and improvement
- 3+ years of experience building production Python systems
- Experience with LLM applications: agent orchestration, prompt engineering, RAG, or similar
- Hands-on experience with agent frameworks (LangGraph, LangChain, CrewAI, AutoGen, or similar)
- Understanding of LLM reasoning patterns and common failure modes
- Proficiency with modern Python web frameworks
- Comfort working in a fast-moving team where priorities evolve
Preferred qualifications
- Experience building semantic search and embedding pipelines
- Hands-on experience with vector databases (e.g., Snowflake Cortex, Pinecone, Weaviate)
- Background in information retrieval
- Experience with search relevance tuning and ranking optimization
- Familiarity with LLM observability tools
- Background in NLP, text summarization, or information extraction
Benefits
- Hybrid/remote work model (about 1-2 days in the office per month).
- A position in a highly professional and globally respected market research and advisory firm, where initiative leading to results is rewarded.
- Individualized Culture: An environment where you can explore new areas outside your specialty and stay engaged with work you enjoy.
Tags & focus areas
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