Senior Data Science Engineer

We are looking for a talented engineer to develop and maintain tools/products aimed at making the data science development work easier. The right candidate will be passionate about learnings, delivering high-quality codes and cutting-edge technologies in Bigdata landscape. The candidate will have evangelical mindset to identify, practice and promote better working methods in the fields of distributed computing to be used in data science engineering.  

What we expect from you  

1. Core Engineering Responsibilities

• Develop and maintain engineering tools/products needed for simple and efficient data science development. 

• Analyse complex data pipelines to identify performance bottlenecks and suggest robust ways to optimize the workload within reasonable costs. 

• Collaborate with Infrastructure and Data Science Platform teams to ensure ensure robust, performant and scalable environments are available for building data science workflows  

1. Data Engineering & Processing Expertise

• Proficiency in Hadoop, PySpark, Pandas, NumPy, and Python (version > 3.5). 

• Strong working experience with data partitioning, joins, caching, handling data skewness, and general code optimization. 

• Solid knowledge of SQL and Airflow (or equivalent orchestration tools like Apache NiFi, Luigi). 

• Experience with file formats and storage optimization (e.g., Parquet, Avro). 

1. Cloud & Kubernetes Knowledge

• Strong working knowledge of Kubernetes and Docker for deploying, managing, and scaling applications. 

• Experience with cloud platforms (Azure/GCP) to leverage Kubernetes services, such as GKE (Google Kubernetes Engine) or AKS (Azure Kubernetes Service), for cloud-based, containerized workflows. 

1. Application Development & Integration

• Experience in developing web applications using modern frontend and backend frameworks (e.g., Node.js, React, Dash, Django, Voila, Streamlit). 

• Proficiency in web application development and API integration (REST APIs, Web services) to support data science tools and workflows. 

1. Automation & DevOps Skills

• Proficiency in shell scripting and working knowledge of DevOps/DataOps practices. 

• Familiarity with CI/CD pipelines and automation for Kubernetes deployments. 

• Experience in re-engineering, automating, and productionizing code, particularly for containerized environments. 

1. Advanced & Good-to-have Skills

• Experience optimizing large-scale NLP or generative models in production, with knowledge of GPU utilization, distributed computing, and cloud-based scaling for efficient model workflows. 

• Exposure to handling unstructured data types, including text and images, with an emphasis on designing and optimizing workflows for large-scale data processing.