Design-led data science and low-code analytics for enterprise data stories
Gramener builds data and AI applications for enterprises on its low-code Gramex platform, pairing analytics with visual storytelling. The stack spans four vector databases (Pinecone, FAISS, Weaviate, Qdrant) and three cloud ML services (SageMaker, Vertex AI, Azure ML), which points to multi-cloud GenAI delivery for clients. Senior-weighted engineering hiring in India and Pakistan, plus recurring MLOps and deployment pain points, shows a push to productionize ML at scale.
Notable leadership hires: Tech Lead
Gramener, founded in 2010 and headquartered in Princeton, NJ, is a privately held data science firm with 201–500 employees. It combines consulting with Gramex, its low-code platform for building data and AI solutions, and serves enterprises in pharmaceuticals, manufacturing, technology and the public sector. Its work covers data visualization, predictive analytics, business intelligence, machine learning and narrative-driven insights. The company operates as part of Straive and has offices in the United States, Canada, India and Singapore.
Gramener uses Python, FastAPI, Django and Flask, Apache Spark and Airflow, PostgreSQL and MongoDB, and vector databases such as Pinecone and Qdrant. It runs on AWS, Azure and GCP with Docker, Kubernetes and GitLab CI/CD.
Gramener is currently hiring in India and Pakistan. Most openings are senior and lead engineering roles, including a Tech Lead position, with smaller needs in data, marketing, sales and design.
Gramener's current work includes data pipeline architecture, an MLOps platform, CI/CD automation, model deployment and monitoring, GenAI-powered data application architecture, and sales force effectiveness (SFE) analytics.
Other companies in the same industry, closest in size
Gramener's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.