AI Senior Engineer- AI Native Engineering

Description - External

• Data Product Engineer (3-8 Years Overall Experience)
• Location: Bengaluru
• Employment Type: Full-Time
• Design, build and operate reusable, governed and scalable data products by integrating data from fragmented enterprise, engineering and manufacturing systems.
• The role will build the actual data layer that abstracts source-system complexity and provides reliable data for applications, analytics, AI/ML and GenAI solutions.

Source Integration

• Connect and ingest data from heterogeneous systems including databases, APIs, files, cloud platforms, enterprise applications, manufacturing systems and event streams.
• Develop batch, incremental, CDC and streaming ingestion pipelines.
• Understand source-system structures and map them into standardized domain models.

Data Engineering

• Build production-grade ETL/ELT pipelines using Python, SQL, PySpark/Spark and relevant cloud technologies.
• Develop transformations from raw/source-aligned data into clean, standardized and reusable business data.
• Build robust orchestration, scheduling, error handling and recovery mechanisms.
• Optimize data pipelines for reliability, scalability, performance and cost.

Data Product Development

• Develop reusable data products rather than application-specific extracts.
• Implement domain and canonical data models defined with the Data Architect.
• Define and implement data-product schemas and contracts.

Expose data through appropriate mechanisms such as

Lakehouse tables

SQL endpoints

Events

Semantic models

Data Quality & Observability

• Build automated data-quality validations.
• Implement monitoring, alerting and observability.
• Handle schema changes and source-system changes.
• Implement lineage and metadata requirements.
• Perform root-cause analysis for pipeline and data-quality failures.

Software Engineering

• Apply Git-based development and branching practices.
• Implement CI/CD for data pipelines.
• Develop automated unit and integration tests.
• Participate in peer reviews and technical design discussions.
• Build reusable engineering components and frameworks.

AI Data Enablement

• Prepare structured and unstructured data for AI/ML and GenAI applications.
• Build pipelines supporting AI use cases and enterprise knowledge layers.
• Exposure to embeddings, vector databases and RAG architectures is desirable.

ETL/ELT

Data modeling

APIs

CI/CD

Data quality and testing

Apache Kafka / Event Hubs

• Snowflake or equivalent
• Good to Have
• Power BI semantic models
• API development
• Event-driven architectures

Docker

• AI/ML pipelines
• Vector databases
• RAG / GenAI data engineering

Preferred Manufacturing Experience

• Manufacturing / automotive data
• MES / ERP / production-planning systems
• Shopfloor and equipment data

IoT/telemetry

Engineering data

Description - External

Job Description

• Data Product Engineer (3-8 Years Overall Experience)
• Location: Bengaluru
• Employment Type: Full-Time
• Design, build and operate reusable, governed and scalable data products by integrating data from fragmented enterprise, engineering and manufacturing systems.
• The role will build the actual data layer that abstracts source-system complexity and provides reliable data for applications, analytics, AI/ML and GenAI solutions.

Key Responsibilities

Source Integration

• Connect and ingest data from heterogeneous systems including databases, APIs, files, cloud platforms, enterprise applications, manufacturing systems and event streams.
• Develop batch, incremental, CDC and streaming ingestion pipelines.
• Understand source-system structures and map them into standardized domain models.

Data Engineering

• Build production-grade ETL/ELT pipelines using Python, SQL, PySpark/Spark and relevant cloud technologies.
• Develop transformations from raw/source-aligned data into clean, standardized and reusable business data.
• Build robust orchestration, scheduling, error handling and recovery mechanisms.
• Optimize data pipelines for reliability, scalability, performance and cost.

Data Product Development

• Develop reusable data products rather than application-specific extracts.
• Implement domain and canonical data models defined with the Data Architect.
• Define and implement data-product schemas and contracts.

Expose data through appropriate mechanisms such as

Lakehouse tables

SQL endpoints

APIs

Events

Semantic models

Data Quality & Observability

• Build automated data-quality validations.
• Implement monitoring, alerting and observability.
• Handle schema changes and source-system changes.
• Implement lineage and metadata requirements.
• Perform root-cause analysis for pipeline and data-quality failures.

Software Engineering

• Apply Git-based development and branching practices.
• Implement CI/CD for data pipelines.
• Develop automated unit and integration tests.
• Participate in peer reviews and technical design discussions.
• Build reusable engineering components and frameworks.

AI Data Enablement

• Prepare structured and unstructured data for AI/ML and GenAI applications.
• Build pipelines supporting AI use cases and enterprise knowledge layers.
• Exposure to embeddings, vector databases and RAG architectures is desirable.

Technical Skills

Must Have

Python

SQL

PySpark / Apache Spark

ETL/ELT

Data modeling

APIs

Git

CI/CD

Data quality and testing

Platform Experience Experience in one or more of

Microsoft Fabric

Azure Data Factory

Azure Databricks

ADLS

Delta Lake

Apache Kafka / Event Hubs

• Snowflake or equivalent
• Good to Have
• Power BI semantic models
• API development
• Event-driven architectures

Docker

• AI/ML pipelines
• Vector databases
• RAG / GenAI data engineering

Preferred Manufacturing Experience

• Manufacturing / automotive data
• MES / ERP / production-planning systems
• Shopfloor and equipment data

IoT/telemetry

Engineering data