AI Engineer- Data & AI

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