Jobs at Tiger Analytics

Leads financial crime compliance business analysis for AML modernization initiatives. Partners with compliance, risk, engineering, and IT teams to define requirements, map banking data, translate regulations into monitoring rules, evaluate vendor capabilities, conduct gap analysis, and manage UAT. Develops BRDs, FSDs, test plans, user stories, and acceptance criteria while supporting transaction monitoring, KYC, sanctions screening, investigations, case management, and regulatory reporting programs.

Designs enterprise-scale data and analytics architectures using Azure, Snowflake, and Databricks. Assesses legacy platforms, develops modernized end-to-end data ecosystems, and establishes data modeling, pipeline, security, governance, privacy, and compliance standards. Partners with senior clients and technology teams to define solutions, lead architecture reviews, guide distributed engineering teams, and drive modernization through automation and reusable frameworks.

Build and deploy production-grade Generative AI and Agentic AI solutions for enterprise clients. Responsibilities include designing LLM applications, RAG systems, AI agents, and multi-agent workflows; integrating enterprise data and applications; deploying and scaling cloud-based systems; and optimizing reliability, security, performance, and cost. The role requires direct client engagement, strong software engineering skills, cloud expertise, and hands-on experience with LLM frameworks, agent orchestration, and production AI operations.

Designs, develops, and maintains scalable Power BI dashboards, semantic models, and reporting infrastructure. Creates optimized DAX measures and time-intelligence logic, transforms data with Power Query, improves report performance, and implements RLS and OLS security. Collaborates with data engineers, analysts, data scientists, AI teams, and business stakeholders to deliver actionable analytics solutions.

Designs, builds, and maintains scalable ETL/ELT pipelines, cloud data infrastructure, storage, and batch or real-time processing workloads. Optimizes queries, PySpark jobs, pipelines, performance, and cloud costs. Partners with BI, analytics, data science, and AI teams to develop dimensional data marts supporting reporting and machine learning. Implements governance, data quality validation, lineage tracking, security controls, and automated infrastructure and code deployments using CI/CD practices.

Deploys, integrates, and scales enterprise generative AI solutions within customer engineering teams. Builds intelligent agents and multi-agent systems using Vertex AI, ADK, and MCP; connects AI systems to enterprise databases; develops real-time data pipelines; and operationalizes LLMs across cloud environments. The role also includes model training, tuning, evaluation, deployment, vector-based grounding, cloud infrastructure integration, stakeholder communication, and translating business goals into technical architectures.

The Azure Architect will design and migrate enterprise technology products to Azure, guiding development teams through all stages while ensuring compliance and security.

Designs, builds, and maintains scalable AWS data pipelines, integrations, lakehouse infrastructure, and Airflow workflows. Processes commercial pharmaceutical data, supports pharma KPIs and analytics, and develops pipelines for AI, machine learning, generative AI, LLM, embeddings, and RAG applications. The role also includes legacy-system migration, pipeline monitoring and optimization, data quality assurance, and translating pharmaceutical business needs into reliable data solutions.

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

The Sales Leader drives revenue growth by managing strategic client relationships, leading presales activities, and winning multi-million-dollar Quality Engineering deals.

Embed with client engineering teams to operationalize LLMs and retrieval systems across multi-cloud environments. Build agentic solutions using Vertex AI Agent Builder and ADK, connect agents securely to BigQuery/Spanner via MCP, implement streaming pipelines (Dataflow) for real-time inference, and manage model training, tuning, deployment, and evaluation on Vertex AI. Provide consultative guidance, cross-functional collaboration, and clear stakeholder communication to ensure business value from Generative AI solutions.

Lead analytics consulting delivery, manage client relationships, drive account growth, and collaborate with teams to develop strategic analytics solutions for CPG and Retail clients.

Lead AI and data transformation initiatives, develop AI strategies, deliver AI/ML solutions, mentor teams, and drive innovation for enterprise clients.

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.