Data and AI Engineer (Manager)
Bengaluru, INDIA, India
INR 2500K-3380K (estimate) Senior-level Full Time
Tasks
- Architect scalable data pipelines for batch and streaming analytics
- Build AI ML data infrastructure including feature stores
- Build CI/CD pipelines and automate deployments
- Build scalable inference pipelines and monitoring
- Design and build enterprise data platforms
- Design data models and partitioning strategies
- Develop and optimize ETL and ELT pipelines
- Ensure data quality, reliability, and observability
- Implement GenAI pipelines for embeddings and vector storage
- Implement infrastructure for high availability and disaster recovery
- Implement streaming data pipelines with Kafka and Spark Streaming
- Manage AWS and Azure cloud infrastructure
- Mentor data engineers and conduct code reviews
- Orchestrate workflows with Airflow Prefect or Control-M
- Support MLOps workflows for model deployment and monitoring
- Translate product and stakeholder requirements into technical solutions
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | AWS Glue | Airflow | Amazon Redshift | Apache Kafka | Apache Spark | Azure | CI/CD | Cassandra | Control-M | Data Modeling | Data Quality | Data pipeline | Data pipeline observability | Databricks | Datadog | Disaster Recovery | Docker | EC2 | ELT | EMR | ETL | Embeddings | Feature Store | Flink | Git | GitHub Actions | GitLab CI | Great Expectations | HBase | HDFS | Hadoop | High Availability | Hive | Infrastructure as Code | Java | Jenkins | Kafka Streams | Kubernetes | Lambda | MLOps | Model Deployment | Model Monitoring | MongoDB | Observability | Pipeline observability | Prefect | Prometheus | Python | RAG | Redis | Retrieval-Augmented Generation | S3 | SQL | SageMaker | SageMaker Endpoints | Scala | Snowflake | Spark SQL | Spark Streaming | TensorFlow Serving | Triton | Vector Database | YARN | “as-code”
Education
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