AI Senior Engineer- AI Native Engineering
Key details
- Location
- Bengaluru, KA, India
- Work style
- Onsite
- Level
- Senior
- Job type
- Full-time
- Experience
- 3–8 yrs
- Function
- Software engineering
- Focus area
- AI / ML
- Posted
- 14h ago
Skills
About the role
Job Description:
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
Job Description:
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
Job Description:
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
Job Description:
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 comple
How well do you match this job?
Free account needed to apply or save. It takes a minute, and we'll bring you right back to this job.