Jobs at Tiger Analytics

Build and deploy production-grade GenAI and Agentic AI solutions for enterprise clients. Responsibilities include designing LLM applications, RAG systems, AI agents, and multi-agent workflows; integrating APIs, databases, and business applications; deploying and scaling cloud solutions; optimizing reliability, security, performance, and cost; and partnering with technical and business stakeholders from initial problem definition through production deployment.

Develop and refactor Python optimization algorithms using object-oriented programming for supply chain, inventory allocation, replenishment, and pricing optimization. Build predictive models, apply advanced statistical and optimization techniques to large datasets, and deliver data-driven solutions for retail and CPG clients. Collaborate with business stakeholders and data science teams, communicate analytical insights, coach colleagues, and apply current data science practices to business problems.

Leads Agile delivery across multiple teams or complex value streams, facilitating Scrum ceremonies, coaching teams, resolving impediments, managing dependencies and risks, and aligning stakeholders. The role oversees backlog and sprint goals, delivery metrics, release and change management, governance, reporting, and continuous improvement. Experience with analytics programs, cloud platforms, and tools such as Azure DevOps, Jira, Confluence, and Miro is valuable.

Leads AI and Generative AI engagements from discovery through production, serving as the client and technical delivery lead. Combines project management with hands-on architecture and development of LLM applications, RAG pipelines, AI agents, APIs, and cloud-based services. Coordinates cross-functional teams, manages risks and delivery milestones, translates business needs into technical solutions, and oversees evaluation, monitoring, deployment, and production readiness.

Manage analytics consulting projects from problem definition through solution delivery. Translate client business challenges into analytics designs, apply statistical and machine learning techniques, present insights to executives, and develop plans to operationalize solutions. Collaborate with global consulting, IT, and data engineering teams while managing stakeholders and project teams.

Designs and deploys enterprise knowledge graph solutions, semantic models, ontologies, taxonomies, and data governance frameworks. Establishes metadata management, lineage, business glossaries, stewardship workflows, and data quality standards. Collaborates with business, AI/ML, and engineering teams to translate domains into scalable architectures and integrate graphs with data fabrics, data meshes, and AI systems. Serves as a technical authority on client deployments and converts ambiguous requirements into production-ready data architecture blueprints.

Looking for a Data Engineer experienced in Dataiku to design and maintain data pipelines and infrastructure, ensuring data flow and integration across teams.

The Data Engineer will architect and implement advanced analytics capabilities, manage data pipelines, and optimize data processing using AWS cloud and various analytical tools.

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 Machine Learning Engineer will create scalable ML systems, build production data pipelines, and ensure robust deployment and monitoring of solutions.

Seeking an experienced Data Engineer to design and maintain data pipelines and infrastructure, collaborating with data scientists and stakeholders.

The role involves engaging with client teams to gather and analyze requirements, managing projects, acting as a point of contact, and collaborating with offshore teams to deliver analytical solutions.

Lead development and refactor optimization algorithms in Python for pricing and supply‑chain problems. Design and implement predictive models and mathematical optimization solutions, collaborate with cross‑functional teams, coach data scientists, and translate business requirements into data‑driven pricing and replenishment strategies.

Manage, design, deploy, optimize, and scale persistent and transient AWS EMR clusters for batch data processing. Automate infrastructure with CloudFormation, Terraform, and CodePipeline; administer EC2, S3, and related AWS services; monitor pipelines; troubleshoot performance issues; and maintain security, patching, access controls, and cluster configurations. Preferred experience includes Hadoop, HDFS, AWS EKS, containerized workloads, and Kerberos.

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.

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.

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.