Teams at Tiger Analytics

Advise business leaders on complex decisions by structuring ambiguous problems, analyzing data, developing insights, and translating recommendations into measurable outcomes. Rapidly prototype solutions using GenAI, agentic AI, analytics, and low-code/no-code tools. Coordinate data scientists, engineers, domain experts, product teams, and AI agents while communicating clearly with senior stakeholders and continuously evaluating emerging technologies.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Embed with client engineering teams to deploy, integrate, and scale enterprise generative AI solutions. Build agentic systems, train and serve LLMs with Vertex AI, integrate models with BigQuery/AlloyDB/Spanner, implement streaming inference pipelines, and ensure operational reliability while collaborating with stakeholders to translate business goals into production-grade architectures.