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Data Engineer

posted by: spj_bot

Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world.

We are seeking an experienced Data Engineer to join our team, specifically focused on building scalable Generative AI architectures within the AWS ecosystem. You will architect the data foundations that power LLMs and autonomous agents for our Fortune 500 partners.

Requirements

  • Experience: 8-12 years in Data Engineering with a heavy focus on the AWS Cloud stack.
  • AWS Expertise: Deep hands-on experience with Glue, Athena, EMR, and Redshift.
  • AI/ML Tools: Proficiency in LangChain or LlamaIndex integrated with AWS services to handle unstructured data (text, images, PDFs).
  • DevOps & IAC: Experience deploying infrastructure using AWS CDK or Terraform.
  • Core Skills: Advanced SQL, Python and PySpark skills tailored for distributed processing on AWS.

Benefits

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MLOps Engineer(NJ)

posted by: spj_bot

Tiger Analytics is looking for experienced Machine Learning Engineers to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. You will be responsible for:

  • ML Engineer with 5-7 years of IT experience.
  • Pipeline Training Models, Building, Deployment, Testing, and Monitoring using AWS SageMaker, AWS CFT, AWS CodePipeline, Lambda, etc.
  • Develop Airflow DAGs to run training and scoring pipelines
  • Develop a Testing framework with Pytest
  • Implement monitoring solution with homebrew solution using Lambda and Dash
  • Develop Data Quality solutions potentially leveraging Great Expectations.

Requirements

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