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Tiger Analytics

Delivers Data and AI transformation

Gen AI Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listedRemote

About the Job

Requirements

  • Proficiency in Python, SQL, and PySpark.
  • Experience with Snowflake, NoSQL, and Neo4j.
  • Proficiency with Apache Airflow.
  • Familiarity with AWS services or GCP services, including the listed cloud tools and platforms.
  • Experience with Linux.
  • Experience building and deploying batch and real-time pipelines.
  • Experience with GitHub.
  • Proficiency with Visual Studio Code.
  • Skills in testing, deployment automation, DevOps, and SysOps.
  • Strong presentation and communication skills.
  • Experience working with onshore and offshore teams.
  • Eight or more years of experience in data engineering, platform engineering, or related fields, with deep expertise in designing and building distributed data systems and large-scale data warehouses.
  • A proven track record of architecting data platforms capable of processing petabytes of data and supporting real-time and batch ingestion processes.
  • Strong experience building robust data pipelines for document ingestion, indexing, and retrieval to support scalable retrieval-augmented generation solutions.
  • Proficiency in information retrieval systems and vector search technologies, such as FAISS, Pinecone, Elasticsearch, and Milvus.
  • Experience with graphs and graph algorithms, large language models, optimization algorithms, relational databases, and diverse data formats.
  • Proficiency in infrastructure and architecture for optimal extraction, transformation, and loading of data from various data sources.
  • Hands-on experience curating and collecting data from traditional and non-traditional sources.
  • Experience building ontologies in the knowledge retrieval space, schema-level constructs including higher-level classes, punning, and property inheritance, and Open Cypher.
  • Experience integrating external databases, APIs, and knowledge graphs into retrieval-augmented generation systems to improve contextualization and response generation.

Responsibilities

  • Design and build distributed data systems and large-scale data warehouses.
  • Architect data platforms capable of processing petabytes of data and supporting real-time and batch ingestion processes.
  • Build data pipelines for document ingestion, indexing, and retrieval to support scalable retrieval-augmented generation solutions.
  • Develop infrastructure and architecture for extracting, transforming, and loading data from various data sources.
  • Curate and collect data from traditional and non-traditional sources.
  • Build ontologies and schema-level constructs in the knowledge retrieval space.
  • Integrate external databases, APIs, and knowledge graphs into retrieval-augmented generation systems.
  • Conduct experiments to evaluate retrieval-augmented generation workflows, analyze results, and iterate to achieve optimal performance.

Desired Qualifications

  • Experience with Hadoop and Spark.
  • Proficiency with Streamlit and dashboards.
  • Experience building and maintaining internal APIs.
  • Basic understanding of machine learning concepts.
  • Familiarity with generative AI tools and techniques.
  • Experience creating and retrieving knowledge graphs.
  • Proficiency managing vector databases.
  • Ability to develop and maintain multiple data persistence and retrieval methods, including relational databases, vector databases, buckets, graph databases, and knowledge graphs.
  • Experience with AWS, especially SageMaker, Lambda, and OpenSearch.
  • Experience with Airflow DAGs, AutoSys, and CronJobs.
  • Experience managing unstructured data, including audio, video, image, and text.
  • Expertise in continuous integration and deployment using Jenkins and GitHub Actions.
  • Advanced skills in Terraform and CloudFormation.
  • Knowledge of Docker and Kubernetes.
  • Ability to monitor system performance, reliability, and security, and optimize them as needed.
  • In-depth understanding of security best practices in cloud environments.
  • Experience designing and managing scalable infrastructure.
  • Knowledge of disaster recovery and business continuity planning.
  • Ability to stay current with industry trends and adapt to new technologies and methodologies.
  • Ability to work effectively in a team environment and contribute to a collaborative culture.

About the Company

Tiger Analytics

Delivers Data and AI transformation

Tiger Analytics helps large organizations plan and execute Data and AI–led transformations at scale. It works with clients to translate business needs into data and AI initiatives, using an approach that combines business value mapping, agile operating models, and a platforms-based architecture, backed by partnerships with major hyperscalers. The company designs and delivers end-to-end data and AI solutions—from strategy and data engineering to model development and deployment—so that AI can be applied to tough, real-world problems. Unlike firms that rely mainly on standard playbooks, Tiger Analytics builds custom approaches and accelerators to suit each client’s value chain and operating model. With a global team of 5000+ technologists and consultants, the goal is to enable measurable value and practical outcomes through data and AI programs that work at enterprise scale.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
San Jose, CA
Founded
2010

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