Tiger Analytics is a global leader in AI and advanced analytics consulting, empowering Fortune 1000 companies to solve their toughest business challenges. We are on a mission to push the boundaries of what AI can do, providing data-driven certainty for a better tomorrow. Our diverse team of over 6,000 technologists and consultants operates across five continents, building cutting-edge ML and data solutions at scale. Join us to do great work and shape the future of enterprise AI.
We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.
Role Overview
The AI Engineer drives the on-site deployment, integration, and scaling of our enterprise Generative AI solutions. This role embeds directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across Azure as cloud. You will bridge the gap between AI research and production-grade cloud infrastructure.
You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.
Requirements
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
Build and deploy production-grade GenAI and machine-learning systems with strategic customers, translating complex business problems into secure, scalable solutions and reusable patterns for enterprise AI.
Own AI evaluation systems and agent infrastructure, using production LLMs, rigorous experimentation, and scalable backend services to measure and improve the quality, reliability, and performance of AI features.
Build and scale evaluation frameworks for multi-agent AI systems, defining quality metrics, safety guardrails, benchmark datasets, and production monitoring to make LLM and RAG workflows reliable.
Build production-ready AI agent capabilities, from Python infrastructure and external integrations to full-stack product features, while improving agent reliability, safety, latency, and cost.
Own Cresta’s marketing web platform and build AI agents for marketing use cases, from architecture and marketer-facing tools to production deployment, evaluation, and feedback that informs product development.
