Data Analysis & Simulation Professional (Gen AI Engineer) Siemens

6.0 - 10.0 years

0 Lacs

karnataka

On-site

You are a highly experienced AI engineer with 5+ years of experience, possessing a strong background in machine learning, proficient programming skills, and a deep understanding of generative models. Your primary role involves applying your expertise to translate research findings into practical solutions that effectively tackle real-world challenges. It is crucial for you to ensure the reliability and ethical usage of generative AI in the applications you develop. In terms of technical requirements, you must exhibit a strong proficiency in Python for data processing and automation. You should have hands-on experience working with generative AI models and integrating them into data workflows. Additionally, familiarity with prompt engineering and LLM models (Opensource and Closesource) is essential. Experience with application development frameworks like LangChain, LangGraph, and working with REST frameworks such as Fast API, Angular, Flask, and Django is highly beneficial. Knowledge of cloud platforms such as AWS, GCP, Azure, and related services is a plus. Moreover, familiarity with containerization and orchestration tools like Docker and Kubernetes would be advantageous. As a Data Analysis & Simulation Professional, your responsibilities include the following key areas: Data Pipeline Development: - Designing and implementing scalable data pipelines using Python to ingest, process, and transform log data from diverse sources. Generative AI Integration: - Collaborating with data scientists to integrate generative AI models into log analysis workflows. - Developing APIs and services to deploy AI models for real-time log analysis and insights generation. Data Monitoring and Maintenance: - Setting up monitoring and alerting systems to ensure the reliability and performance of data pipelines. - Troubleshooting and resolving issues related to data ingestion, processing, and storage. Collaboration and Documentation: - Working closely with cross-functional teams to comprehend requirements and deliver solutions that align with business needs. - Documenting data pipeline architecture, processes, and best practices for future reference and knowledge sharing. Evaluation and Testing: - Conducting thorough testing and validation of generative models. Research and Innovation: - Staying updated with the latest advancements in generative AI and exploring innovative techniques to enhance model capabilities. - Experimenting with different architectures and approaches to drive innovation. Furthermore, having experience with Snowflake utilization would be considered an asset: - Designing and optimizing data storage and retrieval strategies using Snowflake. - Implementing data modeling, partitioning, and indexing strategies to improve query performance.,

Posted 2 days ago

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