Data Engineer+AI Exposure

Role: Data Engineer + AI Exposure

Location : Bangalore

Experience: 7 to 13 Years

Notice: Immediate to 90 days

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Job Summary

We are seeking a skilled Data Engineer with AI/ML exposure responsible for designing, building, and maintaining scalable data pipelines and supporting data-driven applications, including AI/ML use cases. The ideal candidate should have strong expertise in data engineering tools along with working knowledge of machine learning workflows and cloud-based data platforms.

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Key Responsibilities

Data Engineering

• Design, develop, and maintain scalable ETL/ELT pipelines
• Build and optimize data architectures, data lakes, and data warehouses
• Ensure data quality, integrity, and security across systems
• Work with structured and unstructured data from various sources
  
  

Big Data & Cloud

• Develop solutions using tools such as Azure Data Factory / AWS Glue / GCP Dataflow
• Work with big data technologies like Spark, Hadoop, or Databricks
• Manage data storage solutions including S3, ADLS, BigQuery, Snowflake, or Redshift
  
  

AI/ML Exposure

• Support machine learning pipelines and data preparation for ML models
• Collaborate with Data Scientists to enable feature engineering and model deployment
• Work on AI-enabled data solutions (e.g., NLP, recommendation systems, prediction models)
• Basic understanding of ML frameworks (Scikit-learn, TensorFlow, or PyTorch is a plus)
  
  

Data Modeling & Optimization

• Design and implement data models (dimensional & normalized)
• Optimize queries and pipelines for efficiency and cost
  
  

Collaboration & Governance

• Work closely with business teams, analysts, and ML engineers
• Implement data governance, lineage, and compliance standards
• Document workflows, pipelines, and architectures
  
  

Required Skills

Core Data Engineering

• Strong in SQL, Python
• Experience with ETL tools and pipeline orchestration (Airflow, ADF, etc.)
• Hands-on with data warehousing concepts
  
  

Big Data Technologies

• Apache Spark / PySpark
• Hadoop ecosystem (optional but preferred)
  
  

Cloud Platforms (any one required)

• Azure / AWS / GCP hands-on experience
• Familiarity with cloud-native data services
  
  

AI/ML Exposure

• Experience working with data for ML models
• Knowledge of ML lifecycle and data preparation
• Exposure to MLOps concepts (bonus)
  
  

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• Preferred Qualifications
• Experience with Databricks / Snowflake
• Knowledge of API-based data ingestion
• Familiarity with CI/CD pipelines
• Exposure to real-time streaming (Kafka, Event Hub, etc.)
• Understanding of Generative AI or LLM integrations (added advantage)