AI-first product engineering from prototype to enterprise scale
Fission Labs is an AI and product engineering firm building systems where intelligence is architected from day one, not bolted on. The tech stack reveals a dual-track operation: deep ML/LLM infrastructure (TensorFlow, PyTorch, Hugging Face, LLMs via Gemma and Mistral) paired with AWS cloud-native tooling (Lambda, API Gateway, CloudFormation), now adopting LangChain and LlamaIndex to operationalize RAG at scale. The hiring acceleration (8 roles in 30 days, 70% engineering-focused with 4 leads) signals capacity expansion for larger client engagements.
Notable leadership hires: Sales Director
Fission Labs delivers end-to-end AI product engineering for startups and enterprises, from ideation through cloud-scale deployment. Founded in 2008, the company operates a 350+ person organization positioned as an AWS and Databricks partner, with a track record of shipping 150+ products and helping clients through Series B funding and acquisition exits. Their work spans RAG pipelines, fine-tuned models, AI agents, cloud infrastructure, and full-stack product development. The project backlog emphasizes moving prototypes to production, hybrid cloud architecture, and GPU cluster deployment for training and inference workloads.
Core ML: Python, TensorFlow, PyTorch, Hugging Face Transformers, BERT, GPT. Cloud: AWS (Lambda, API Gateway, VPC, CloudFront, IAM, CloudWatch, CloudFormation). Recently adopting: TensorFlow Lite, MediaPipe, Gemma, Mistral, LangChain, LlamaIndex. Languages: Kotlin, Android SDK, Bash.
Active projects include AI integration, prototype-to-production workflows, offline-first product experiences, AWS disaster recovery, GitLab-to-GitHub migration, CI/CD pipeline development, Docker/Kubernetes-based ML app deployment, and GPU cluster infrastructure for AI training and inference.
Fission Labs'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.