AI Data Engineer
Data EngineerData
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Quick Summary
Technical Tools
Data EngineerData
Requirements
~1 min read- 7–18 years of experience in Data Engineering, AI Data Engineering, or related engineering roles.
- Strong proficiency in Python, SQL, and data processing frameworks.
- Hands-on experience with Apache Spark, Kafka, Airflow, and distributed data systems.
- Experience building large-scale ETL/ELT pipelines and modern data architectures.
- Strong understanding of AI/ML data workflows and Generative AI infrastructure.
- Experience with cloud platforms (AWS, Azure, or GCP).
- Knowledge of vector databases, embeddings, RAG pipelines, and unstructured data processing.
- Experience with data warehouses, data lakes, and lakehouse platforms.
- Strong understanding of database technologies, data modelling, and optimization techniques.
- Familiarity with Docker, Kubernetes, CI/CD, and infrastructure automation.
Responsibilities
~1 min read- Design, develop, and maintain scalable ETL/ELT pipelines for processing large volumes of structured and unstructured data.
- Build reliable batch and real-time data processing systems to support AI/ML applications.
- Develop data ingestion, transformation, integration, and orchestration frameworks.
- Optimize data workflows for performance, scalability, and cost efficiency.
- Build and maintain data infrastructure supporting machine learning, deep learning, and Generative AI applications.
- Develop data pipelines for model training, validation, testing, and deployment.
- Work with vector databases, embeddings, and retrieval-augmented generation (RAG) architectures.
- Prepare, clean, transform, and validate datasets for AI model development.
- Support feature engineering, feature stores, and model data management.
- Design scalable data architectures using modern data platforms and distributed computing frameworks.
- Work with cloud environments such as AWS, Microsoft Azure, or Google Cloud Platform.
- Implement data lakes, data warehouses, and lakehouse architectures.
- Leverage technologies such as Apache Spark, Kafka, Airflow, Databricks, Snowflake, and Delta Lake.
- Establish data quality checks, monitoring, validation, and observability frameworks.
- Ensure data consistency, integrity, security, and compliance.
- Implement access controls, metadata management, and data governance practices.
- Identify and resolve data pipeline bottlenecks and infrastructure issues.
- Collaborate with AI engineers, ML engineers, data scientists, and product teams.
- Lead technical discussions, architecture decisions, and engineering best practices.
- Mentor junior engineers and drive continuous improvements in data engineering processes.
- Evaluate emerging AI and data technologies to improve platform capabilities.
- Experience working with LLMs and AI-powered applications.
- Exposure to LangChain, LlamaIndex, and model-serving infrastructure.
- Knowledge of MLOps, feature stores, and model lifecycle management.
- Experience with Databricks, Snowflake, BigQuery, or Redshift.
- Familiarity with data observability and governance tools.
Location & Eligibility
Where is the job
Bangalore, India
On-site at the office
About this job
- Posted
- Today
Listing Details
- Posted
- November 9, 2026
- First seen
- November 9, 2026
- Last seen
- November 9, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 60%
- Scored at
- November 9, 2026
Signal breakdown
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