Data Analysis Opportunity 2025 | Siemens is hiring Data Analysis . The working location is Bengaluru, India. Interested candidates can apply online as soon as possible. The detailed eligibility and application process are given below.
Data Analysis Job Openings At Siemens | Bengaluru – Overview
| Company Name: | Siemens |
| Job Role: | Data Analysis |
| Location: | Bengaluru |
| Work Mode: | On-site |
| Last Date to Apply: | ASAP |
Job Description
- We are seeking for a 5–7-year experience AI engineer with a strong background in machine learning, programming skills, and a deep understanding of generative models. The position is responsible for turning research into practical solutions that address real-world problems while ensuring the reliability and ethical use of generative AI in their applications.
Skills Required:
- Strong proficiency in Python for data processing and automation.
- Handson experience with generative AI models and their integration into data workflows.
- Handson experience with prompt engineering and LLM models (Opensource and Closesource)
- Handson experience with Application development framework like LangChain, LangGraph etc.
- Familiarity working with REST frameworks like Fast API, Angular, Flask and DJango.
- Experience with cloud platforms (AWS, GCP, Azure) and related services is a plus.
- Familiarity with containerization and orchestration tools (Docker, Kubernetes).
Responsibility:
- As a Data Analysis & Simulation Professional, the person will be responsible for:
- Data Pipeline Development:
- Design and implement scalable data pipelines using Python to ingest, process, and transform log data from various sources.
- Generative AI Integration:
- Collaborate with data scientists to integrate generative AI models into the log analysis workflow.
- Develop APIs and services to deploy AI models for real-time log analysis and insights generation.
- Data Monitoring and Maintenance:
- Set up monitoring and alerting systems to ensure the reliability and performance of data pipelines.
- Troubleshoot and resolve issues related to data ingestion, processing, and storage.
- Collaboration and Documentation:
- Work closely with cross-functional teams to understand requirements and deliver solutions that meet business needs.
- Document data pipeline architecture, processes, and best practices for future reference and knowledge sharing.
