Data Analysis & Simulation Professional (Gen AI Engineer)

We are seeking for a 4-7-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. Technical Requirements: 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). 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. Evaluation and Testing: Conduct thorough testing and validation of generative models. Research and Innovation: Stay updated with the latest advancements in generative AI and explore innovative techniques to enhance model capabilities. Experiment with different architectures and approaches. Snowflake Utilization: (Good to have) Design and optimize data storage and retrieval strategies using Snowflake. Implement data modeling, partitioning, and indexing strategies to enhance query performance.