Aller au contenu Aller au pied de page

GenAI engineer

Référence du poste
513738
Publié depuis
16-Jul-2026
Domaine d'activité
Recherche et développement
Entreprise
Siemens Healthcare Private Limited
Niveau d'expérience
Expérimenté
Type de poste
Temps plein
Mode de travail
Au bureau / sur site uniquement
Type de contrat
Contrat à durée indéterminée (CDI)
Localisation(s)
  • Bangalore - Karnataka - Inde
Technical skills requirement - Experienced Gen-AI Engineer 

• Strong proficiency in Python for data processing and automation. Ability to write efficient and well-structured 
code.
• Experienced with generative AI models and their integration into data workflows.
• Experienced with prompt engineering and LLM models (Opensource and licensed. Ex Llama, OpenAI) 
• Experienced with Application development framework like LangChain or similar frameworks.
• Experienced working with REST frameworks like Fast API, Flask and Django.
• Good understanding of machine learning workflows and deployment. 
• Knowledge of ETL processes, data modeling, and data warehousing principles.
• Familiarity with containerization and orchestration tools (Docker, Kubernetes).
• Familiarity with Snowflake for data warehousing and analytics.
• Experienced with cloud platforms (AWS, GCP, Azure) and related services is a plus.
• Strong communication and collaboration skills.
• Excellent problem-solving skills and attention to detail.
Restricted © Siemens Healthineers, 2024
Key Responsibilities:
1. Data Pipeline Development:
o Design and implement scalable data pipelines using Python to ingest, process, and transform log data from various sources.
2. Generative AI Integration:
o Collaborate with data scientists to integrate generative AI models into the log analysis workflow.
o Develop APIs and services to deploy AI models for real-time log analysis and insights generation.
3. Data Monitoring and Maintenance:
o Set up monitoring and alerting systems to ensure the reliability and performance of data pipelines.
o Troubleshoot and resolve issues related to data ingestion, processing, and storage.
4. Collaboration and Documentation:
o Work closely with cross-functional teams to understand requirements and deliver solutions that meet business needs.
o Document data pipeline architecture, processes, and best practices for future reference and knowledge sharing.
 Evaluation and Testing: 
o Conduct thorough testing and validation of generative models
 Snowflake Utilization: 
o Design and optimize data storage and retrieval strategies using Snowflake.
o Implement data modeling, partitioning, and indexing strategies to enhance query performance.
 Research and Innovation: 
o Stay updated with the latest advancements in generative AI and explore innovative techniques to enhance model capabilities. 
o Experiment with different architectures and approaches like Agentic AI