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ProductSquads Linkedin · Posted 16d ago

AI Engineer- Data & AI

Hyderabad, Telangana, India

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Indexed description

About the Role


We are looking for a seasoned data engineer with extensive experience in AI platforms and analytics to join our team. The ideal candidate will have a strong background in designing and operating AI-enabled platforms, as well as a proven track record of managing end-to-end data and content lifecycles.


Required Qualification-


  • 10+ years in data engineering, AI platforms, analytics, or large-scale content systems
  • 5+ years designing or operating AI-enabled platforms on AWS, GCP
  • Advanced Python skills, including building production-grade web services (e.g. FastAPI, Flask)
  • Excellent SQL skills and experience supporting low-latency and analytical workloads
  • Proven ownership of end-to-end data and content lifecycles
  • Hands-on experience implementing automation-first and AI-assisted workflows, reducing manual intervention
  • Experience working with global, distributed teams and enterprise-scale data environments
  • Experience supporting multiple products or domains concurrently in a matrix environment
  • Direct engagement with senior stakeholders to translate business problems into AI-enabled data capabilities
  • Comfort owning systems in production, including monitoring, incident response, and performance tuning


Qualifications

  • Bachelor’s or Master’s degree in:
  • Computer Science
  • Data Science
  • Statistics
  • Mathematics
  • Engineering
  • Related discipline


Required Skills

  • Experience in regulated, compliance-driven or data-intensive environments (financial services, consulting, healthcare, telecom, energy, or global enterprises)
  • Exposure to content management, document intelligence, or unstructured data analytics
  • Experience embedding analytics into products, platforms, or client-facing services


Preferred Skills

  • Experience building data-backed APIs with authentication, authorization, and rate limiting
  • Familiarity with infrastructure-as-code (Terraform, CDK, or CloudFormation)
  • Exposure to ML-adjacent workloads (feature generation, offline training data)
  • Experience with schema evolution and data versioning in product environment

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