Data Science & GenAI Production Support Engineer
Hyderabad, IndiaFull-timeMid · 2-5 years
About this role
Position Summary
We are seeking a proactive and technically strong Data Science & GenAI Production Support Engineer to provide production support for our Data Science and Generative AI solutions. The ideal candidate will be responsible for ensuring the stability, reliability, and performance of AI and data science applications while driving root cause analysis, continuous improvements, and AI-enabled operational efficiencies.
Key Responsibilities
- Provide production support for Data Science and Generative AI applications.
- Monitor, troubleshoot, and resolve production issues within defined SLAs.
- Perform root cause analysis (RCA) for incidents and implement preventive measures.
- Support deployment, maintenance, and optimization of data science models in production.
- Work closely with Data Scientists, AI Engineers, and Platform teams to ensure smooth production operations.
- Develop automation and operational improvements to enhance support efficiency.
- Leverage AI tools and technologies to improve support workflows and accelerate issue resolution.
- Contribute to AI proof of concepts (POCs) and identify opportunities to integrate AI into operational processes.
- Maintain operational documentation, knowledge articles, and support runbooks.
- Participate in on-call support and incident management activities as required.
Required Skills
- Experience in Data Science Model Production Support.
- Strong understanding of Root Cause Analysis (RCA) and production incident management.
- Hands-on experience with Neo4j.
- Knowledge of Agentic AI concepts and applications.
- Strong proficiency in Python, PySpark, and SQL.
- Experience with the Microsoft Azure data ecosystem, including:
- Azure Databricks
- Azure Data Factory (ADF)
- Azure Data Lake Storage (ADLS)
- Ability to troubleshoot data pipelines and production data issues.
- Strong analytical, problem-solving, and communication skills.
- Familiarity with MLOps and model lifecycle management is an added advantage.