F
Factored Tech Stack
AI engineering staffing and team-building for enterprise ML/data projects
Factored places and builds AI engineering teams for tech companies, focusing on data science, ML, and AI roles. The stack reveals heavy investment in GenAI tooling (LangChain, LangGraph, OpenAI, RAG) alongside traditional ML infrastructure (PyTorch, TensorFlow, SageMaker, Databricks), while active projects center on agentic workflows and RAG pipeline optimization — indicating the company is actively building, not just recruiting. The hiring mix skews heavily senior (9 of 11 roles), suggesting Factored sources and places experienced practitioners rather than entry-level talent.
Tech Stack 43 technologies
Core StackAWS Lambda SageMaker LangChain OpenAI RAG FastAPI Django Flask PostgreSQL Python SQL Server Azure Data Factory Snowflake Power BI dbt Tableau Databricks AWS Apache Airflow Cassandra MongoDB PyTorch TensorFlow AWS ECS LangGraph SQL Azure Synapse Analytics R Keras GCP+13 more
What Factored Is Building
◆Challenges
- Scaling generative ai systems
- Ensuring compliance and privacy
- Improving data pipeline efficiency
- Complex multi-source datasets
- Complex real-world problems
- Performance improvement of rag models
- Deployment into production environments
- Closing multi-million-dollar partnerships
- Driving arr growth
- Scaling before exit
▲Active Projects
- Agentic ai workflow architecture
- Cloud-native ai application deployment on aws
- Rag pipeline optimization
- Design and develop frameworks for genai products
- Build and implement machine learning models for client growth
- Deploy rag models into production environments
- Financial reporting automation integration
- Board-level financial presentations
Hiring Activity
Minimal10 roles · 1 in 30d
Department
Seniority
Company intelligence
