Applied AI systems for multi-agent reasoning and real-time deployment
Big AIR Lab builds agentic AI systems that coordinate across multiple LLMs and reasoning engines. The stack spans Python + FastAPI + Node.js on cloud infrastructure (AWS/GCP/Azure), with heavy use of Hugging Face, OpenAI, and Anthropic models, plus local inference via vLLM and Ollama. Current projects—RAG pipelines, agentic application deployment, real-time audio/video—suggest a shift from research-stage prototypes toward production systems; the hiring mix (mid-level engineering dominant, minimal velocity) reflects a lean organization navigating the pilot-to-scale transition.
Big AIR Lab is a 11–50 person AI services firm based in Bengaluru, India, focused on applied AI systems that require multi-model orchestration and contextual adaptation. The engineering-forward team (6 engineers, 3 product, support ops) builds across mobile (Flutter/Dart), backend (Python FastAPI), and web (React/Next.js) surfaces. Active engagements span RAG pipeline development, agentic application deployment, evaluation tooling, and real-time communication features. The organization is currently scaling from pilot deployments to production workloads, managing rapid model iteration cycles and resource constraints typical of this phase.
OpenAI and Anthropic for production inference, plus Hugging Face for open-source model access and local inference via vLLM and Ollama for latency-sensitive or on-premise deployments.
Python + FastAPI backend; Flutter + Dart for mobile; React + Next.js for web; Firebase and Supabase for data layer; Docker and GitHub Actions for CI/CD; deployed on AWS, GCP, and Azure.
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