• Own end-to-end delivery: Design and build full-stack features from UI to backend to deployment. • Ship real AI systems: Implement RAG, tool-calling, agent workflows, structured prompting, evals, and guardrails. • Move fast, responsibly: Prototype quickly, then harden for security, reliability, and cost control. • Use AI as a force multiplier: Work fluently with tools like Cursor, Claude Code, Windsurf, or Copilot to accelerate delivery while maintaining code quality. • Make technical calls: Choose patterns, trade-offs, and scope boundaries without waiting for permission. • Collaborate tightly: Work directly with strategists, clients, and other senior engineers to unblock progress. • Light DevOps ownership: Configure environments, CI/CD, secrets, and deployments (with support when complexity grows).

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 a highly skilled **GenAI Engineer** with strong hands-on experience in building, evaluating, and deploying advanced Generative AI systems. The ideal candidate will have deep expertise in agentic frameworks, model fine-tuning, and reinforcement learning, along with a strong focus on experimentation, reliability, and hallucination mitigation beyond prompt engineering.

• Design and lead the development of large-scale, cloud-native Generative AI platforms and solutions. • Architect and design cloud-native, scalable GenAI platforms leveraging Microsoft Azure services. • Define end-to-end system architectures integrating GenAI components with enterprise applications. • Design and govern API-first architectures, including REST/GraphQL APIs, service contracts, and integration patterns. • Implement security-by-design principles, including identity and access management, network security, data protection, and compliance standards. • Design and optimize event-driven architectures using messaging, streaming, and asynchronous processing patterns. • Lead architectural decisions around scalability, performance, reliability, and cost optimization. • Collaborate closely with engineering, data, AI, and platform teams to ensure successful implementation of architectural designs. • Provide technical leadership, mentorship, and architectural guidance across teams. • Establish best practices, standards, and reference architectures for GenAI solutions. • Review and approve solution designs to ensure alignment with enterprise and cloud architecture standards.

• Architect, build, and maintain production-grade software (services, APIs, and web applications) with strong engineering discipline: code quality, testing, observability, and operational readiness. • Lead design reviews, establish engineering standards, and mentor engineers across multiple projects—especially software-heavy initiatives. • Build integration layers and service patterns that connect applications to data platforms, identity systems, and enterprise tooling. • Drive modern SDLC practices: trunk-based development or equivalent, strong code review culture, automated testing, and predictable release workflows. • Design and operate Kubernetes-based application platforms, including deployment standards, networking/ingress, upgrades, reliability, and day-2 operations. • Implement and mature CI/CD pipelines and release automation for both applications and infrastructure (including GitOps patterns where appropriate). • Build and maintain infrastructure-as-code (e.g., Terraform) and automated environment provisioning in Azure or AWS. • Lead engineers in deploying and maintaining enterprise platform tools such as Starburst, Immuta, Collibra, Databricks, Synapse, and related services. • Develop plans for cloud migrations and deployments and execute modernization strategies for application and data workloads. • Build and maintain data pipelines and supporting services that enable analytics, ML, and AI-enabled applications. • Partner with stakeholders to shape practical approaches to AI agents, AI infrastructure, and AI application enablement (e.g., orchestration patterns, retrieval/knowledge integration, evaluation and monitoring) in a secure environment.