BrightAI builds a sensor-to-insights platform for critical infrastructure operators using computer vision (YOLO, TensorRT) and LLMs (Claude, GPT, Llama) on a stateful backend. The tech stack reveals a mature embedded AI architecture—PyTorch + TensorFlow for model training, TensorFlow Lite for edge deployment, Kafka for real-time data streams, and vector DBs (Weaviate, Pinecone, FAISS) for RAG systems. Hiring is heavily weighted toward staff and senior engineers (13 of 17 roles), with active projects around real-time visual intelligence and LLM-powered industrial troubleshooting, signaling both technical depth and scaling ambitions. Pain points cluster around latency-sensitive inference and data pipeline scalability—typical friction when moving from research to production AI at edge.
Tech Stack 98 technologies
What BrightAI Is Building
◆Challenges
- Optimizing storage performance and cost
- Real-time inference optimization
- Compensation framework for growth
- Latency-sensitive llm applications
- Scalable llm applications
- Scaling data ingestion pipelines
- Data privacy compliance
- High-priority roles in ai/ml
- Complex pipelines with competing priorities
- Scaling recruiting processes
▲Active Projects
- Real-time visual intelligence
- Next-generation ai systems at scale
- Retrieval-augmented generation (rag) systems
- Ai-powered assistants for industrial troubleshooting
- Document ingestion and semantic search pipelines
- Scalable data ingestion pipelines
- Data lake and sql-based storage architecture
- Internal data ingestion tools and frameworks
- Automation framework implementation
- Qa strategy development
