Location
IN
About the Role
You have over 10 years of development experience and around 5 years of experience working with data integration, data engineering, big data technologies or similar. You also have experience architecting complex systems at scale and possess a good overview of data engineering technologies like Spark, Airflow/Conductor, Kafka, ETL/ELT, Blob storage/Parquet/Iceberg/Delta tables, and Langgraph. Additionally, you have experience in building products that utilize probabilistic technologies such as machine learning or AI. Your strong software development background includes typed & compiled languages, and experience with JVM is considered a plus. You have implemented good practices for testing and development standards across large codebases and understand how to design high-performance systems that consistently deliver on SLOs. Your experience also includes working with k8s and managed services (PaaS) on major public clouds like AWS, Azure, or GCP, as well as working with OAuth2.
The company's Office of the CTO is responsible for the architecture of organization Data Fusion, ensuring a robust and scalable platform for solving industrial data problems for customers. They are seeking a highly skilled and motivated software architect to lead the design, development, and evolution of the data integration architecture. This architecture encompasses data ingestion, staging, transformation, contextualization, and data pipeline orchestration, aiming to provide a long-term competitive advantage while maintaining scalability, robustness, and cost efficiency.
Your key responsibilities will include:
The company's Office of the CTO is responsible for the architecture of organization Data Fusion, ensuring a robust and scalable platform for solving industrial data problems for customers. They are seeking a highly skilled and motivated software architect to lead the design, development, and evolution of the data integration architecture. This architecture encompasses data ingestion, staging, transformation, contextualization, and data pipeline orchestration, aiming to provide a long-term competitive advantage while maintaining scalability, robustness, and cost efficiency.
Your key responsibilities will include:
Ideal Candidate
Someone who has spent 5+ years shipping data platforms (Spark, Kafka, Airflow) at scale and recently pivoted into AI/ML product design—not a pure ML researcher, but an engineer who sees LLMs and agents as infrastructure problems.
Estimated Salary Range(low confidence)
₹24 L – ₹42 L per year
Likely Interview Questions
- 1.Walk us through a data integration architecture you designed—what were the top 3 cost and latency tradeoffs you made, and how did they change when AI/ML requirements entered the picture?
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