Tiger Analytics Inc.

AI Engineers deploy, integrate, and scale enterprise generative AI solutions for customers across Azure, combining UiPath automation, agentic workflows, retrieval systems, and LLM fine-tuning. The role is highly hands-on and customer-embedded, bridging AI research with production cloud infrastructure.

Tiger Analytics is seeking a Forward Deployed Engineer to work directly with clients and engineering teams to build, deploy, and scale production-grade GenAI and agentic AI solutions. The role combines customer problem-solving with hands-on development of LLM applications, RAG systems, AI agents, and multi-agent workflows.

Tiger Analytics is seeking a Forward Deployed Engineer to partner with clients and build production-grade GenAI and agentic AI solutions from initial problem definition through deployment. The role combines customer-facing consulting with hands-on development of LLM applications, RAG systems, AI agents, and multi-agent workflows.

Lead the architecture and delivery of a Snowflake-native RAG AI Assistant while coordinating an offshore engineering pod and client stakeholders. The role combines hands-on Python and multi-agent systems engineering with AI governance, UAT, and production launch ownership.

Lead enterprise AI and Generative AI engagements from client discovery through production while balancing project leadership with hands-on engineering. Build LLM applications, RAG systems, AI agents, and production AI services for major clients.

Lead enterprise AI and Generative AI engagements from discovery through production while balancing client and project leadership with hands-on engineering. Build LLM-powered applications, RAG systems, AI agents, and production-grade AI services for major clients.

Lead the end-to-end delivery of enterprise Agentic AI solutions for the Luma platform while serving as the primary technical interface for customers. Build and deploy Neo4j Knowledge Graph, RAG, and AI applications from rapid prototypes through production.

Lead enterprise MLOps on the Databricks Lakehouse platform by designing, automating, governing, and observing end-to-end machine learning production lifecycles. This hybrid role also serves as a client-facing technical authority responsible for enabling onsite engineering teams.