AI Data Engineer

Job Description

Job Title: AI Data Engineer(Data & ML Pipelines)

Experience: 8–14 Years

Location: Multiple locations across India (Hybrid – 3 days office)

Employment Type: Full-Time,29276

Notice Period: Immediate to 15 days preferred

Role Overview

We are looking for a strong AI Engineer with solid data engineering expertise to design, build, and operationalize scalable data and AI solutions. This role combines advanced AI/ML development with enterprise-grade data pipeline architecture. You will work on real-world AI use cases while ensuring robust, production-ready data platforms.

Key Responsibilities

• Architect and maintain scalable data pipelines and enterprise data models for AI, analytics, and BI use cases
• Design and optimize ETL/ELT frameworks using modern cloud-based data integration tools
• Build and manage cloud data platforms for high-performance analytics
• Develop and deploy AI/ML solutions including deep learning, NLP, LLMs, and generative AI
• Lead AI experimentation, prototyping, and applied research initiatives
• Build end-to-end ML pipelines (data prep → feature engineering → deployment → monitoring)
• Implement CI/CD workflows for data and AI pipelines
• Create executive dashboards and analytical applications for business insights
• Collaborate with cross-functional teams to align technical solutions with business needs
• Define and enforce best practices in data quality, governance, security, and observability
  
  

• Strong hands-on experience with AI/ML techniques including Deep Learning, NLP, and LLMs / Generative AI
• Proven experience delivering AI-driven solutions for real business problems
• Strong Python expertise for data engineering and machine learning
• Hands-on experience with Snowflake for cloud data warehousing and analytics
• Experience building data pipelines using modern ETL/ELT tools (e.g., Azure Data Factory or similar)
• Strong understanding of enterprise data modeling, data quality, and scalable pipeline design
• Experience implementing CI/CD pipelines for ML and data workflows
• Experience building dashboards using Power BI or Tableau
• Experience working with cloud platforms (Azure preferred; AWS/GCP acceptable)
• Ability to translate technical outcomes into business insights
• Solid exposure to data engineering + AI engineering together (not purely one side)
  
  

• Experience with MLOps practices (model deployment, monitoring, observability)
• Exposure to telecom/network data (5G, LTE, VoLTE, SMS, usage, roaming, etc.)
• Familiarity with vector databases, embeddings, and RAG architectures
• Experience integrating LLMs with enterprise systems and data platforms
• Experience working in Agile and large enterprise environments
  
  

• Strong career stability
• Notice period within 15 days
• Experience aligned with stated years of experience
• Compensation expectations aligned with the budget range
  
  

• 2 Technical Rounds
• 1 Client/Stakeholder Round