Hyderabad, Telangana, India
Information Technology
Full-Time
Cyient
Overview
As a Data Engineer, you will design and implement data pipelines and integrations across cloud platforms to support AI and GenAI workloads. You’ll work closely with Data Scientists and ML Engineers to enable GenAI-powered applications through clean, secure, and optimized data flows.
Key Responsibilities - Data Engineering
Build and maintain ETL/ELT pipelines for structured, semi-structured, and unstructured data.
Work with data warehouses/lakehouses (Snowflake, BigQuery, Databricks, Redshift).
Develop real-time streaming pipelines using Kafka, Spark, or Flink.
Ensure data quality, validation, and error handling.
Prepare and manage data pipelines to feed LLMs and GenAI models.
Work with vector databases (FAISS, Pinecone, Weaviate, Milvus) for RAG-based solutions.
Support embedding generation and prompt engineering workflows.
Collaborate with AI teams to integrate GenAI APIs and frameworks (LangChain, Hugging Face, OpenAI).
Deploy pipelines on cloud services (AWS Glue, Azure Data Factory, GCP Dataflow/Dataproc).
Use Airflow, Dagster, or Prefect for orchestration.
Required Skills
Proficiency in Python, SQL, PySpark.
Hands-on experience with ETL pipelines and data modeling.
Knowledge of cloud data platforms (AWS, Azure, or GCP).
Experience with vector databases and GenAI frameworks
Familiarity with Docker, Kubernetes, CI/CD pipelines.
Preferred Skills
Exposure to MLOps / LLMOps practices.
Experience with semantic search, embeddings, or RAG systems.
Familiarity with data security, compliance, and governance.
Key Responsibilities - Data Engineering
Build and maintain ETL/ELT pipelines for structured, semi-structured, and unstructured data.
Work with data warehouses/lakehouses (Snowflake, BigQuery, Databricks, Redshift).
Develop real-time streaming pipelines using Kafka, Spark, or Flink.
Ensure data quality, validation, and error handling.
Prepare and manage data pipelines to feed LLMs and GenAI models.
Work with vector databases (FAISS, Pinecone, Weaviate, Milvus) for RAG-based solutions.
Support embedding generation and prompt engineering workflows.
Collaborate with AI teams to integrate GenAI APIs and frameworks (LangChain, Hugging Face, OpenAI).
Deploy pipelines on cloud services (AWS Glue, Azure Data Factory, GCP Dataflow/Dataproc).
Use Airflow, Dagster, or Prefect for orchestration.
Required Skills
Proficiency in Python, SQL, PySpark.
Hands-on experience with ETL pipelines and data modeling.
Knowledge of cloud data platforms (AWS, Azure, or GCP).
Experience with vector databases and GenAI frameworks
Familiarity with Docker, Kubernetes, CI/CD pipelines.
Preferred Skills
Exposure to MLOps / LLMOps practices.
Experience with semantic search, embeddings, or RAG systems.
Familiarity with data security, compliance, and governance.
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