Jobs at Tiger Analytics

Develop and refactor Python optimization algorithms for supply chain, inventory allocation, replenishment, and pricing optimization. Build predictive models, analyze large datasets, design experiments, monitor KPIs, and provide data-driven recommendations. Collaborate with business stakeholders and data science teams to deliver analytical solutions, while coaching colleagues and applying advanced statistical and operations research methods.

Leads client-facing AI and Generative AI engagements from discovery through production while combining project leadership with hands-on engineering. Responsibilities include architecting and deploying LLM applications, RAG systems, AI agents, and production AI services; coordinating technical teams; managing delivery risks and timelines; and translating business needs into practical AI solutions. The role requires expertise in Python, cloud platforms, LLM frameworks, vector databases, APIs, microservices, deployment, evaluation, monitoring, and responsible AI.

Leads technology advisory, client engagement, presales, solution architecture, and practice development for enterprise data and AI transformations. Provides thought leadership, assesses client platforms, develops modernization roadmaps, creates architectures and proposals, and mentors engineering, data, and architecture teams. Requires deep hands-on Databricks and Azure expertise, experience with enterprise Lakehouse platforms, streaming and batch pipelines, governance, security, and large-scale transformation programs. Partners with senior executives and offshore teams to drive delivery quality, talent development, and consulting growth.

Designs and deploys advanced AI and machine learning solutions, including AI agents, RAG systems, analytical models, and data products on Google Cloud Platform. Responsibilities include preparing structured and unstructured data, developing AI agent tools, applying NLP and deep learning, collaborating with customers and stakeholders, generating business insights, and communicating analytical results.

Lead hands-on architecture, development, deployment, and optimization of enterprise GenAI, RAG, Agentic AI, and Neo4j Knowledge Graph solutions. Build data pipelines, APIs, microservices, and cloud-native AI infrastructure; rapidly deliver customer POCs and production systems. Serve as the primary technical interface for enterprise clients, troubleshooting performance, scalability, reliability, and cost issues while providing technical consulting.

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.

The Databricks Architect will design and implement large-scale data solutions, optimizing performance and reliability, while leading initiatives in data engineering.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

The Technology Partner will lead technology solutions and data transformation initiatives for Fortune 100 clients, providing thought leadership and managing executive relationships.

Lead design and delivery of Snowflake-centric cloud data platforms. Engage with clients to gather requirements, architect scalable secure solutions, provide technical leadership, develop roadmaps, advise executives, and guide implementation best practices across the analytics stack.

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.

The Databricks Architect will design and implement data architecture solutions using Databricks, AWS, and Snowflake, focusing on MLOps and data governance.

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

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

The Gen AI Data Engineer will design and build distributed data systems, develop data pipelines, manage data infrastructure, and integrate technologies for real-time and batch processing, contributing to scalable analytics solutions.

Lead portfolio-level Data & AI strategy and technical governance for enterprise clients. Advise C-suite, drive architecture and modern data stacks, win high-value engagements, and scale engineering and data science teams.

Lead design and governance of enterprise ontologies, knowledge graph architecture, ingestion pipelines, and semantic layers. Enable semantic interoperability across data products, implement inference and reasoning, and establish standards, versioning, and data contracts to support scalable graph-driven analytics and AI augmentation.

Lead business development and growth in AI/ML, provide technical leadership in data science solutions, and enhance client engagement by delivering strategic data insights.

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.

The Sales Director will develop and execute sales strategies for consulting services, manage relationships with technology partners, and grow the client base for Tiger Analytics.