Jobs at Tiger Analytics

Define enterprise AI strategy and architecture for LLMs, RAG, and predictive models; design secure, high-performance REST/GraphQL APIs and event-driven systems; establish AI/data governance; and lead cross-functional teams to productionize real-time AI applications.

As a Senior Agentic AI Engineer, you will lead innovation in AI initiatives, from ideation to prototyping, collaborating with teams to deliver impactful solutions.

As a Machine Learning Engineer, you'll develop scalable ML solutions using Google Cloud Platform and Vertex AI, collaborate with teams, and operationalize models from ingestion to monitoring.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

The Finance Director will lead the finance team, overseeing financial planning, analysis, and management practices to support the company's growth and performance optimization.

Lead design, development, and deployment of GenAI and NLP solutions (RAG, LLM-powered apps, agentic AI, Document Intelligence). Work with clients to translate business needs, build end-to-end ML pipelines, mentor teams, present results to stakeholders, and operationalize scalable, governed AI systems.

Assess and document current and target Martech architecture, identify redundancies and integration gaps, define unified data/process flows and integration patterns, recommend consolidation and AI-driven platform strategy, support implementation planning and governance, and produce architecture documentation to guide execution teams.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

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.

The GenAI Engineer will design, build, and deploy Generative AI solutions, focusing on reinforcement learning and multi-agent workflows, while collaborating with various teams to enhance model performance.

Design and lead the development of cloud-native Generative AI platforms and solutions, ensuring scalability, security, and integration with enterprise applications.

Design, build, and deploy scalable ML solutions using GCP and Vertex AI. Collaborate with teams to operationalize models and monitor performance.